The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Thomas Richardson - One of the best experts on this subject based on the ideXlab platform.
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generation of window traversing flyable trajectories using Logistic Curve
International Conference on Unmanned Aircraft Systems, 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
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Generation of Window-Traversing Flyable Trajectories Using Logistic Curve *
2020 International Conference on Unmanned Aircraft Systems (ICUAS), 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
Zhao Yulin - One of the best experts on this subject based on the ideXlab platform.
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APPLICATION OF Logistic Curve IN ELECTRIC POWER LOAD FORECAST
Power system technology, 2004Co-Authors: Zhao YulinAbstract:On the basis of the research results published in existing relevant references, the Logistic Curve method is further improved and a new method to estimate the parameters of the Logistic Curve is given. In this method the optimization method and the regression method are integrated, a series of values of a are obtained by interval search and one dimension search, and a series of values of parameters a and b which are corresponding to the values of α are obtained by regression method. When α reaches its optimal value, the parameters a and b will be their optimal values. The calculation results of the data from actual distribution power network show that the forecasted results by the proposed method are more accurate.
Saurabh Upadhyay - One of the best experts on this subject based on the ideXlab platform.
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generation of window traversing flyable trajectories using Logistic Curve
International Conference on Unmanned Aircraft Systems, 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
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Generation of Window-Traversing Flyable Trajectories Using Logistic Curve *
2020 International Conference on Unmanned Aircraft Systems (ICUAS), 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
Jonathan Köhler - One of the best experts on this subject based on the ideXlab platform.
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Technological change in energy systems: Learning Curves, Logistic Curves and input–output coefficients
Ecological Economics, 2007Co-Authors: Haoran Pan, Jonathan KöhlerAbstract:Abstract Learning Curves have recently been widely adopted in climate-economy models to incorporate endogenous change of energy technologies, replacing the conventional assumption of an autonomous energy efficiency improvement. However, there has been little consideration of the credibility of the learning Curve. The current trend that many important energy and climate change policy analyses rely on the learning Curve means that it is of great importance to critically examine the basis for learning Curves. Here, we analyse the use of learning Curves in energy technology, usually implemented as a simple power function. We find that the learning Curve cannot separate the effects of price and technological change, cannot reflect continuous and qualitative change of both conventional and emerging energy technologies, cannot help to determine the time paths of technological investment, and misses the central role of R&D activity in driving technological change. We argue that a Logistic Curve of improving performance modified to include R&D activity as a driving variable can better describe the cost reductions in energy technologies. Furthermore, we demonstrate that the top-down Leontief technology can incorporate the bottom-up technologies that improve along either the learning Curve or the Logistic Curve, through changing input–output coefficients. An application to UK wind power illustrates that the Logistic Curve fits the observed data better and implies greater potential for cost reduction than the learning Curve does.
Arthur Richards - One of the best experts on this subject based on the ideXlab platform.
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generation of window traversing flyable trajectories using Logistic Curve
International Conference on Unmanned Aircraft Systems, 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
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Generation of Window-Traversing Flyable Trajectories Using Logistic Curve *
2020 International Conference on Unmanned Aircraft Systems (ICUAS), 2020Co-Authors: Saurabh Upadhyay, Arthur Richards, Thomas RichardsonAbstract:This work considers point-to-point flyable trajectory generation through constrained planar regions referred as windows. A four parameter Logistic Curve trajectory planning tool is proposed for generating feasible trajectories that (i) connect the given initial and final positions with (ii) smooth bounded velocity and acceleration variations (flyability), and (iii) pass through the prescribed windows. A two-step approach is proposed, first narrowing down the search space to a restricted set in six dimensions, parameterizing a variety of trajectories that satisfy the derived closed-form flyability conditions. The final solutions are found by sampling that set using closed-form window traversal conditions. Numerical examples are presented to analyze the performance of the proposed approach in terms of number of solutions and computation time.
