Voltage Stability Monitoring Based on Disagreement-based Deep Learning in a Time-Varying Environment

Author(s):  
Tong Wu ◽  
Ying Jun Zhang ◽  
He Wen
1991 ◽  
Vol 10 (3) ◽  
pp. 228-239 ◽  
Author(s):  
Boris Aronov ◽  
Steven Fortune ◽  
Gordon Wilfong

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.


1986 ◽  
Vol 108 (3) ◽  
pp. 285-291
Author(s):  
M. S. King ◽  
J. K. Blundell

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.


2020 ◽  
Vol 218 ◽  
pp. 108165
Author(s):  
Xiujun Sun ◽  
Ying Zhou ◽  
Hongqiang Sang ◽  
Peiyuan Yu ◽  
Shuai Zhang

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