Password Based a Generalize Robust Security System Design Using Neural Network

Among the various means of available resource protection including biometrics, password based system is most simple, user friendly, cost effective and commonly used. But this method having high sensitivity with attacks. Most of the advanced methods for authentication based on password encrypt the contents of password before storing or transmitting in physical domain. But all conventional cryptographic based encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency. Multi-application usability of password today forcing users to have a proper memory aids. Which itself degrades the level of security. In this paper a method to exploit the artificial neural network to develop the more secure means of authentication, which is more efficient in providing the authentication, at the same time simple in design, has given. Apart from protection, a step toward perfect security has taken by adding the feature of intruder detection along with the protection system. This is possible by analysis of several logical parameters associated with the user activities. A new method of designing the security system centrally based on neural network with intrusion detection capability to handles the challenges available with present solutions, for any kind of resource has presented

Backpropagating neurons from bichromatic interaction with a three-level system

Optical implementation of a backpropagating neuron by means of a nonlinear Fabry-Perot etalon requires thresholding a forward signal beam while the transmittance of a backpropagating beam is multiplied by the differential of the forward signal. This is achievable by inputting a bichromatic field to a three-level system in an optical cavity. The response characteristics of this device have the added possibility of adaptability of the threshold by the backward probe input intensity

Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons

Enhancing reliability in passive anti-islanding protection schemes for distribution systems with distributed generation

This thesis introduces a new approach to enhance the reliability of conventional passive anti-islanding protection scheme in distribution systems embedding distributed generation. This approach uses an Islanding-Dedicated System (IDS) per phase which will be logically combined with the conventional scheme, either in blocking or permissive modes. Each phase IDS is designed based on data mining techniques. The use of Artificial Neural Networks (ANNs) enables to reach higher accuracy and speed among other data mining techniques. The proposed scheme is trained and tested on a practical radial distribution system with six-1.67 MW Doubly-Fed Induction Generators (DFIG-DGs) wind turbines. Various scenarios of DFIG-DG operating conditions with different types of disturbances for critical breakers are simulated. Conventional passive anti-islanding relays incorrectly detected 67.3% of non-islanding scenarios. In other words, the security is as low as 32.3%. The obtained results indicate that the proposed approach can be used to theoretically increase the security to 100%. Therefore, the overall reliability of the system is substantially increased

A Neuroevolutionary Approach to Stochastic Inventory Control in Multi-Echelon Systems

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form

Deep Learning for Black-Box Modeling of Audio Effects

Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often difficult because it seeks to accurately model all components within the effect unit, which usually contains various nonlinearities and time-varying components. Most existing methods for audio effects modeling are either simplified or optimized to a very specific circuit or type of audio effect and cannot be efficiently translated to other types of audio effects. Recently, deep neural networks have been explored as black-box modeling strategies to solve this task, i.e., by using only input–output measurements. We analyse different state-of-the-art deep learning models based on convolutional and recurrent neural networks, feedforward WaveNet architectures and we also introduce a new model based on the combination of the aforementioned models. Through objective perceptual-based metrics and subjective listening tests we explore the performance of these models when modeling various analog audio effects. Thus, we show virtual analog models of nonlinear effects, such as a tube preamplifier; nonlinear effects with memory, such as a transistor-based limiter and nonlinear time-varying effects, such as the rotating horn and rotating woofer of a Leslie speaker cabinet

Using deep neural networks for detecting spurious oscillations in discontinuous Galerkin solutions of convection-dominated convection-diffusion equations

Standard discontinuous Galerkin (DG) finite element solutions to convection-dominated con- vection-diffusion equations usually possess sharp layers but also exhibit large spurious oscillations. Slope limiters are known as a post-processing technique to reduce these unphysical values. This paper studies the application of deep neural networks for detecting mesh cells on which slope limiters should be applied. The networks are trained with data obtained from simulations of a standard benchmark problem with linear finite elements. It is investigated how they perform when applied to discrete solutions obtained with higher order finite elements and to solutions for a different benchmark problem