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The path is defined as a set of waypoints to be passed by the vehicle.
The path selection schemes differ in the way the cost of a path is defined and in the assumption made about the knowledge of the traffic demands.
As the combination of concerns from robotics and sensor networks, a viable path is defined, which is smooth, collision free with sensor nodes and obstacles, closed and let the robot to read all the data from sensor nodes.
The WIRES algorithm creates transects from randomised starting points that terminate at boundary points; the path is defined with directional constraints that collectively ensure that the environmental variability within the domain of study is accessed and sampled.
In this paper, the concept of shortest path is defined on the basis of a scale-free dynamic and stochastic network model, and a temporal ant colony optimization (TACO) algorithm is proposed for searching for the shortest paths in the network.
The pre-compensation value at any given location along the desired path is defined as a weighted average of contour errors within a prediction horizon, and the weights are selected to minimize the sum of squares of the estimated contour errors over the chosen prediction horizon.
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The path was defined using a custom built LabVIEW program to control the output of a spatial light modulator.
Then, the cost of a protein path was defined by gathering up the cost of each edge, which is the P value sum of two interacted protein nodes.
Once the reprogramming path was defined, the potentials of cell states on the path were estimated by a sampling strategy, which avoids the difficulty of estimating the landscape for all possible cell states but still sheds light on understanding how the reprogramming proceeds from a landscape point of view.
Once the A0 modes are extracted from all paths in the transducer network, the relative time delay of the A0 mode in each path with respect to the other paths is defined as a delamination sensitive feature.
(Entropy of Paths) The entropy of the paths is defined as S = - ∑ γ ∈ Γ p log p , (12 according to Shannon [13].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com