Ensemble learning for predicting degradation under time‐varying environment

2020 ◽  
Vol 36 (4) ◽  
pp. 1205-1223
Author(s):  
Lizhi Wang ◽  
Dawei Lu ◽  
Xiaohong Wang ◽  
Rong Pan ◽  
Zhuo Wang
Keyword(s):  
Ensemble Learning ◽  
Time Varying ◽  
Time Varying Environment ◽  
Varying Environment

Minimum-Speed Motions

1991 ◽  
Vol 10 (3) ◽  
pp. 228-239 ◽  
Author(s):  
Boris Aronov ◽  
Steven Fortune ◽  
Gordon Wilfong
Keyword(s):  
Maximum Velocity ◽  
Initial Position ◽  
Linear Constraints ◽  
Initial Configuration ◽  
Full Description ◽  
Speed Limit ◽  
Time Varying ◽  
Moving Obstacles ◽  
Time Varying Environment ◽  
Varying Environment

We consider the problem of determining how fast an object must be capable of moving for it to be able to reach a given position at a given time while avoiding moving obstacles. The problem is to plan velocity profile along a given path so that collisions with moving obstacles crossing the path are avoided and the maximum velocity along the path is mini mized. Suppose the time-varying environment is fully speci fied, both in space and in time, by n linear constraints. An algorithm is presented that, given a full description of the environment and the initial configuration of the system (that is, initial position and starting time of the object), answers in O(log n) time queries of the form : "What is the lowest speed limit that the object can obey while still being able to reach the query configuration from the initial configuration without colliding with the obstacles?" The algorithm can also be used to compute a motion from the initial configuration to the query configuration that obeys the speed limit in O (n ) time. The algorithm requires O (n log n) preprocessing time and O (n) space.

Simulated On-Line Process Optimization of a Robotic Materials Handling System

1986 ◽  
Vol 108 (3) ◽  
pp. 285-291
Author(s):  
M. S. King ◽  
J. K. Blundell
Keyword(s):  
Process Optimization ◽  
Kinematic Model ◽  
Industrial Robots ◽  
Time Varying ◽  
Optimization Strategy ◽  
Materials Handling ◽  
On Line ◽  
Line Process ◽  
Time Varying Environment ◽  
Varying Environment

Industrial robots in use today lack the total ability to perceive and interact with their environment. This limitation is a major obstacle confronting robotic systems developers. This work outlines an on-line process optimization strategy which allows a robot to work within a time-varying environment. After developing the kinematic model of the robot and its relationship to its environment, the process optimization strategy is simulated. The performance of the system is measured by using an index of performance and comparing the simulation results against a series of non-optimized models. The results indicate that on-line process optimization strategy significantly increases the performance of a robotic system operating in a time-varying environment.

Adaptive path following control for Wave gliders in time-varying environment

2020 ◽  
Vol 218 ◽  
pp. 108165
Author(s):  
Xiujun Sun ◽  
Ying Zhou ◽  
Hongqiang Sang ◽  
Peiyuan Yu ◽  
Shuai Zhang
Keyword(s):  
Path Following ◽  
Time Varying ◽  
Path Following Control ◽  
Time Varying Environment ◽  
Varying Environment
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