Long range science scheduling for the Hubble Space Telescope

Observations with NASA's Hubble Space Telescope (HST) are scheduled with the assistance of a long-range scheduling system (SPIKE) that was developed using artificial intelligence techniques. In earlier papers, the system architecture and the constraint representation and propagation mechanisms were described. The development of high-level automated scheduling tools, including tools based on constraint satisfaction techniques and neural networks is described. The performance of these tools in scheduling HST observations is discussed

Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Application Of Multi-Layered Perceptron Neural Network (MLPNN) With Consideration Losses And Emission Dispatch

Nowadays, optimum generation and consumption of energy are considered as two important problems. Economic Dispatch that is the most optimum is one of important problems in power system. This paper presents a multi-layered perceptron neural network (MLPNN) method to solve the combined Losses and emission dispatch problem. Therefore, knowing Problem-Solving in Economic Dispatch is a necessity. First, the Economic Dispatch is explained and then neural network is reviewed. Next, different kinds of neural networks are mentioned by using in Economic Dispatch

Security-constrained Optimal Rescheduling of Real Power using Hopfield Neural Network

A new method for security-constrained corrective rescheduling of real power using the Hopfield neural network is presented. The proposed method is based on solution of a set of differential equations obtained from transformation of an energy function. Results from this work are compared with the results from a method based on dual linear programming formulation of the optimal corrective rescheduling. The minimum deviations in real power generations and loads at buses are combined to form the objective function for optimization. Inclusion of inequality constraints on active line flow limits and equality constraint on real power generation load balance assures a solution representing a secure system. Transmission losses are also taken into account in the constraint function

Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Literal Perceptual Inference

In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module