Stability of multispecies communities in randomly varying environment

1975 ◽  
Vol 2 (2) ◽  
pp. 165-178 ◽  
Author(s):  
G. S. Ladde ◽  
D. D. Šiljak
Keyword(s):  
Varying Environment

Uncertainty associated with river health assessment in a varying environment: The case of a predictive fish-based index in France

2014 ◽  
Vol 43 ◽  
pp. 195-204 ◽  
Author(s):  
Anahita Marzin ◽  
Olivier Delaigue ◽  
Maxime Logez ◽  
Jérôme Belliard ◽  
Didier Pont
Keyword(s):  
Health Assessment ◽  
River Health ◽  
Varying Environment

Population dynamics in a periodically varying environment

1976 ◽  
Vol 56 (2) ◽  
pp. 459-475 ◽  
Author(s):  
R.M. Nisbet ◽  
W.S.C. Gurney
Keyword(s):  
Population Dynamics ◽  
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.

Optimal Resource Allocation in a Randomly Varying Environment

1996 ◽  
Vol 10 (5) ◽  
pp. 602 ◽  
Author(s):  
W. S. C. Gurney ◽  
D. A. J. Middleton
Keyword(s):  
Resource Allocation ◽  
Optimal Resource Allocation ◽  
Optimal Resource ◽  
Varying Environment

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.

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