Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

Tractable theories for the synthesis of neural networks

The Radius of Direct attraction of a discrete neural network is a measure of stability of the network. it is known that Hopfield networks designed using Hebb's Rule have a radius of direct attraction of Omega(n/p) where n is the size of the input patterns and p is the number of them. This lower bound is tight if p is no larger than 4. We construct a family of such networks with radius of direct attraction Omega(n/root plog p), for any p greater than or equal to 5. The techniques used to prove the result led us to the first polynomial-time algorithm for designing a neural network with maximum radius of direct attraction around arbitrary input patterns. The optimal synaptic matrix is computed using the ellipsoid method of linear programming in conjunction with an efficient separation oracle. Restrictions of symmetry and non-negative diagonal entries in the synaptic matrix can be accommodated within this scheme.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Origin of structural defects in multiwall carbon nanotube

We investigate the walls of the defective multiwall carbon nanotube (MWCNT), and give possible mechanism for the formation of defective structure. A generalized model has been proposed for the MWCNT. which consists of (a) catalyst part, (b) embryo part and (c) full grown part. We claim that the weak embryo portion of the MWCNT, is structurally undeveloped. The stress due to pressure imbalance between inside and outside of the MWCNT during growth along with axial load at the embryo portion causes distortion, which is the source of bending and making the walls of the MWCNT off-concentric. At the later stage the stressed embryo retain the distorted structure and get transformed into fully gown defective CNT. Published by Elsevier B.V.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Mutations in Drosophila Myosin Rod Cause Defects in Myofibril Assembly

The roles of myosin during muscle contraction are well studied, but how different domains of this protein are involved in myofibril assembly in vivo is far less understood. The indirect flight muscles (IFMs) of Drosophila melanogaster provide a good model for understanding muscle development and function in vivo. We show that two missense mutations in the rod region of the myosin heavy-chain gene, Mhc, give rise to IFM defects and abnormal myofibrils. These defects likely result from thick filament abnormalities that manifest during early sarcomere development or later by hypercontraction. The thick filament defects are accompanied by marked reduction in accumulation of flightin, a myosin binding protein, and its phosphorylated forms, which are required to stabilise thick filaments. We investigated with purified rod fragments whether the mutations affect the coiled-coil structure, rod aggregate size or rod stability. No significant changes in these parameters were detected, except for rod thermodynamic stability in one mutation. Molecular dynamics simulations suggest that these mutations may produce localised rod instabilities. We conclude that the aberrant myofibrils are a result of thick filament defects, but that these in vivo effects cannot be detected in vitro using the biophysical techniques employed. The in vivo investigation of these mutant phenotypes in IFM development and function provides a useful platform for studying myosin rod and thick filament formation generically, with application to the aetiology of human myosin rod myopathies. (C) 2012 Elsevier Ltd. All rights reserved.

Flux tube model signals for baryon correlations in heavy ion collisions

The flux tube model offers a pictorial description of what happens during the deconfinement phase transition in QCD. The three-point vertices of a flux tube network lead to formation of baryons upon hadronization. Therefore, correlations in the baryon number distribution at the last scattering surface are related to the preceding pattern of the flux tube vertices in the quark-gluon plasma, and provide a signature of the nearby deconfinement phase transition. I discuss the nature of the expected signal, and how to extract it from the experimental data for heavy ion collisions at RHIC and LHC.

Nonstoichiometry, defects and thermodynamic properties of YFeO3, YFe2O4 and Y3Fe5O12

Oxygen nonstoichiometry of three ternary oxides. YFeO3-delta, YFe2O4-alpha and Y3Fe5O12-theta. in the system Y-Fe-O was investigated as a function of oxygen partial pressure by thermogravimetry at high temperature. The defects responsible for nonstoichiometry were identified as oxygen vacancies for YFeO3-delta and YFe2O4-alpha although the manner of variation of nonstoichiometric parameter with oxygen partial pressure for these two oxides is quite different. Cation interstitials are the predominant defects in Y3Fe5O12-theta. Gibbs energies of formation of the three nonstoichiometric oxides were determined using solid-state electrochemical cells in the temperature range from 975 to 1475 K. YFe2O4-alpha was found to be stable only above 1391 K. Gibbs energies of formation of the three stoichiometric compounds from their component binary oxides were obtained by combining information from solid state cells with results of thermogravimetric analysis using the Gibbs-Duhem relation. The results can be summarized as: (1/2)Y2O3 + (1/2)Fe2O3 -> YFeO3;Delta G(f(ox))(O)(+/- 250)(J/mol) = 17, 126-8.263T (1/2)Y2O3 + FeO + (1/2)Fe2O3 -> YFe2O4;Delta G(f(ox))(O)(+/- 260)(J/mol) = -10,352-13.24T (3/2)Y2O3 + (5/2)Fe2O3 -> Y3Fe5O12;Delta G(f(ox))(O)(+/- 780)(J/mol) = -56, 647-31.091T. (C) 2012 Elsevier B.V. All rights reserved.