Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Publication
- Publication Type
Articles 1 - 10 of 10
Full-Text Articles in Computer Engineering
Quantization Of Fractional Constrained Systems With Wkb Approximation, Ola A. Jarabah
Quantization Of Fractional Constrained Systems With Wkb Approximation, Ola A. Jarabah
Applied Mathematics & Information Sciences
In this paper the constrained systems with two primary first class constraints are studied using fractional Lagrangian, after that we find the fractional Hamiltonian and the corresponding Hamilton Jacobi equation. Using separation of variables technique, we can find the action function S this function helps us to formulate the wave function which describe the behavior of our systems also from the action function S we can find the equations of motion and the corresponding momenta in fractional form. This work is illustrated using one example.
Research On Modeling And Scheduling Of Virtual Power Plant With Dual Demand Response, Qiang Chen, Yi Wang, Kangshun Li
Research On Modeling And Scheduling Of Virtual Power Plant With Dual Demand Response, Qiang Chen, Yi Wang, Kangshun Li
Journal of System Simulation
Abstract: Virtual power plant technology provides an effective means to aggregate distributed power and user side resources to participate in power scheduling. Most of the existing research focus on the scheduling optimization of distributed energy instead of the demand response of user side. The user side resources are divided into contracted reliable response load and non-contracted random response load, and the load response is regulated through price adjustment mechanism to adapt to the change of distributed. A virtual power plant optimal scheduling model with dual demands response is constructed, in which the maximizing overall profit of the power grid is …
Formal Language Constraints In Deep Reinforcement Learning For Self-Driving Vehicles, Tyler Bienhoff
Formal Language Constraints In Deep Reinforcement Learning For Self-Driving Vehicles, Tyler Bienhoff
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
In recent years, self-driving vehicles have become a holy grail technology that, once fully developed, could radically change the daily behaviors of people and enhance safety. The complexities of controlling a car in a constantly changing environment are too immense to directly program how the vehicle should behave in each specific scenario. Thus, a common technique when developing autonomous vehicles is to use reinforcement learning, where vehicles can be trained in simulated and real-world environments to make proper decisions in a wide variety of scenarios. Reinforcement learning models, however, have uncertainties in how the vehicle acts, especially in a previously …
Efficient Geophysical Technique Of Vertical Line Elements As A Natural Consequence Of General Constraints Techniques, Rolando Cardenas, Martine Ceberio
Efficient Geophysical Technique Of Vertical Line Elements As A Natural Consequence Of General Constraints Techniques, Rolando Cardenas, Martine Ceberio
Departmental Technical Reports (CS)
Adding Constraints -- A (Seemingly Counterintuitive But) Useful Heuristic In Solving Difficult Problems, Olga Kosheleva, Martine Ceberio, Vladik Kreinovich
Adding Constraints -- A (Seemingly Counterintuitive But) Useful Heuristic In Solving Difficult Problems, Olga Kosheleva, Martine Ceberio, Vladik Kreinovich
Departmental Technical Reports (CS)
Intuitively, the more constraints we impose on a problem, the more difficult it is to solve it. However, in practice, difficult-to-solve problems sometimes get solved when we impose additional constraints and thus, make the problems seemingly more complex. In this methodological paper, we explain this seemingly counter-intuitive phenomenon, and we show that, dues to this explanation, additional constraints can serve as a useful heuristic in solving difficult problems.
Towards An Efficient Bisection Of Ellipsoids, Paden Portillo, Martine Ceberio, Vladik Kreinovich
Towards An Efficient Bisection Of Ellipsoids, Paden Portillo, Martine Ceberio, Vladik Kreinovich
Departmental Technical Reports (CS)
Constraints are often represented as ellipsoids. One of the main advantages of such constrains is that, in contrast to boxes, over which optimization of even quadratic functions is NP-hard, optimization of a quadratic function over an ellipsoid is feasible. Sometimes, the area described by constrains is too large, so it is reasonable to bisect this area (one or several times) and solve the optimization problem for all the sub-areas. Bisecting a box, we still get a box, but bisecting an ellipsoid, we do not get an ellipsoid. Usually, this problem is solved by enclosing the half-ellipsoid in a larger ellipsoid, …
Constraint-Related Reinterpretation Of Fundamental Physical Equations Can Serve As A Built-In Regularization, Vladik Kreinovich, Juan Ferret, Martine Ceberio
Constraint-Related Reinterpretation Of Fundamental Physical Equations Can Serve As A Built-In Regularization, Vladik Kreinovich, Juan Ferret, Martine Ceberio
Departmental Technical Reports (CS)
Many traditional physical problems are known to be ill-defined: a tiny change in the initial condition can lead to drastic changes in the resulting solutions. To solve this problem, practitioners regularize these problem, i.e., impose explicit constraints on possible solutions (e.g., constraints on the squares of gradients). Applying the Lagrange multiplier techniques to the corresponding constrained optimization problems is equivalent to adding terms proportional to squares of gradients to the corresponding optimized functionals. It turns out that many optimized functionals of fundamental physics already have such squares-of-gradients terms. We therefore propose to re-interpret these equations -- by claiming that they …
Why Ellipsoid Constraints, Ellipsoid Clusters, And Riemannian Space-Time: Dvoretzky's Theorem Revisited, Karen Villaverde, Olga Kosheleva, Martine Ceberio
Why Ellipsoid Constraints, Ellipsoid Clusters, And Riemannian Space-Time: Dvoretzky's Theorem Revisited, Karen Villaverde, Olga Kosheleva, Martine Ceberio
Departmental Technical Reports (CS)
In many practical applications, we encounter ellipsoid constraints, ellipsoid-shaped clusters, etc. A usual justification for this ellipsoid shape comes from the fact that many real-life quantities are normally distributed, and for a multi-variate normal distribution, a natural confidence set (containing the vast majority of the objects) is an ellipsoid. However, ellipsoid appear more frequently than normal distributions (which occur in about half of the cases). In this paper, we provide a new justification for ellipsoids based on a known mathematical result -- Dvoretzky's Theorem.
How To Take Into Account Dependence Between The Inputs: From Interval Computations To Constraint-Related Set Computations, With Potential Applications To Nuclear Safety, Bio- And Geosciences, Martine Ceberio, Scott Ferson, Vladik Kreinovich, Sanjeev Chopra, Gang Xiang, Adrian Murguia, Jorge Santillan
How To Take Into Account Dependence Between The Inputs: From Interval Computations To Constraint-Related Set Computations, With Potential Applications To Nuclear Safety, Bio- And Geosciences, Martine Ceberio, Scott Ferson, Vladik Kreinovich, Sanjeev Chopra, Gang Xiang, Adrian Murguia, Jorge Santillan
Departmental Technical Reports (CS)
In many real-life situations, in addition to knowing the intervals Xi of possible values of each variable xi, we also know additional restrictions on the possible combinations of xi; in this case, the set X of possible values of x=(x1,..,xn) is a proper subset of the original box X1 x ... x Xn. In this paper, we show how to take into account this dependence between the inputs when computing the range of a function f(x1,...,xn).
H-Infinity Estimation For Fuzzy Membership Function Optimization, Daniel J. Simon
H-Infinity Estimation For Fuzzy Membership Function Optimization, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. …