Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

Preliminary results from the Caribbean Acoustic Tracking Network (CATn): a data sharing partnership for acoustic tracking and movement ecology of marine animals in the Caribbean Sea

We have recently exchanged and integrated into a single database tag detections for conch, teleost and elasmobranch fish from four separately maintained arrays in the U.S. Virgin Islands including the NMFS queen conch array (St. John nearshore), NOAA’s Biogeography Branch array (St. John nearshore & midshelf reef); UVI shelf edge arrays (Marine Conservation District, Grammanik & other shelf edge); NOAA NMFS Apex Predator array COASTSPAN (St. John nearshore). The integrated database has over 7.5 million hits. Data is shared only with consent of partners and full acknowledgements. Thus, the summary of integrated data here uses data from NOAA and UVI arrays under a cooperative agreement. The benefits of combining and sharing data have included increasing the total area of detection resulting in an understanding of broader scale connectivity than would have been possible with a single array. Partnering has also been cost-effectiveness through sharing of field work, staff time and equipment and exchanges of knowledge and experience across the network. Use of multiple arrays has also helped in optimizing the design of arrays when additional receivers are deployed. The combined arrays have made the USVI network one of the most extensive acoustic arrays in the world with a total of 150+ receivers available, although not necessarily all deployed at all times. Currently, two UVI graduate student projects are using acoustic array data.

Inference and Labeling of Metric-Induced Network Topologies

The development and deployment of distributed network-aware applications and services over the Internet require the ability to compile and maintain a model of the underlying network resources with respect to (one or more) characteristic properties of interest. To be manageable, such models must be compact, and must enable a representation of properties along temporal, spatial, and measurement resolution dimensions. In this paper, we propose a general framework for the construction of such metric-induced models using end-to-end measurements. We instantiate our approach using one such property, packet loss rates, and present an analytical framework for the characterization of Internet loss topologies. From the perspective of a server the loss topology is a logical tree rooted at the server with clients at its leaves, in which edges represent lossy paths between a pair of internal network nodes. We show how end-to-end unicast packet probing techniques could b e used to (1) infer a loss topology and (2) identify the loss rates of links in an existing loss topology. Correct, efficient inference of loss topology information enables new techniques for aggregate congestion control, QoS admission control, connection scheduling and mirror site selection. We report on simulation, implementation, and Internet deployment results that show the effectiveness of our approach and its robustness in terms of its accuracy and convergence over a wide range of network conditions.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

Evaluating the effectiveness of community-based dementia care networks:The Dementia Care Networks' Study

Purpose: The Dementia Care Networks' Study examined the effectiveness of four community-based, not-for-profit dementia networks. The study involved assessing the relationship between the types of administrative and service-delivery exchanges that occurred among the networked agencies and the network members' perception of the effectiveness of these exchanges. Design and Methods: With the use of a case-study method, the evolution, structure, and processes of each network were documented. Social network analysis using a standardized questionnaire completed by member agencies identified patterns of administrative and clinical exchanges among networked agencies. Results: Differences were found between the four networks in terms of their perceptions of service-delivery effectiveness; perceptions of administrative effectiveness did not factor significantly. Exchanges between groups of agencies (cliques) within each of the four networks were found to be more critical than those between individual agencies within each network. Implications: Integration-measured by the types of exchanges within as opposed to across networks-differentiated the four networks studied. This research contributes to our understanding of the use of multiple measures to evaluate the inner workings of service delivery and their impact on elder health and elder health care. Copyright 2005 by The Gerontological Society of America.

Intrusion Detection System for Network Security in Synchrophasor Systems

Synchrophasor systems will play a crucial role in next generation Smart Grid monitoring, protection and control. However these systems also introduce a multitude of potential vulnerabilities from malicious and inadvertent attacks, which may render erroneous operation or severe damage. This paper proposes a Synchrophasor Specific Intrusion Detection System (SSIDS) for malicious cyber attack and unintended misuse. The SSIDS comprises a heterogeneous whitelist and behavior-based approach to detect known attack types and unknown and so-called ‘zero-day’ vulnerabilities and attacks. The paper describes reconnaissance, Man-in-the-Middle (MITM) and Denial-of-Service (DoS) attack types executed against a practical synchrophasor system which are used to validate the real-time effectiveness of the proposed SSIDS cyber detection method.

SWAT-1: The effectiveness of a 'site visit' intervention on recruitment rates in a multi-centre randomised trial

Background: Recruitment rates in multi-centre randomised trials often fall below target recruitment rates, causing problems for study outcomes. The Studies Within A Trial (SWAT) Programme, established by the All-Ireland Hub for Trials Methodology Research in collaboration with the Medical Research Council Network of Hubs in the United Kingdom and others, is developing methods for evaluating aspects of trial methodology through the conduct of research within research. A recently published design for a SWAT-1 provides a protocol for evaluating the effect of a site visit by the principal investigator on recruitment in multi-centre trials.

Methods: Using the SWAT-1 design, the effect of a site visit, with the sole purpose of discussing trial recruitment, on recruitment rates in a large multicentre trial in the Republic of Ireland was evaluated. A controlled before and after intervention comparison was used, where the date of the site visit provides the time point for the intervention, and for the comparison to control sites. Site A received the intervention. Site B and Site C acted as the controls. Z-scores for proportions were calculated to determine within site recruitment differences. Odds ratios and 95% confidence intervals were calculated to determine between site recruitment differences.

Results: Recruitment rates were increased in Site A post-intervention (17% and 14% percentage point increases at 1 and 3 months, respectively). No differences in recruitment occurred in Site B or in Site C. Comparing between site differences, at 3 months post-intervention, a statistically significant difference was detected in favour of higher recruitment in Site A (34% versus 25%; odds ratio 1.57, 95% confidence interval 1.09 to 2.26).

Conclusions: This is the first reported example of a study in the SWAT programme.. It provides evidence that a site visit, combined with a scheduled meeting, increases recruitment in a clinical trial. Using this example, other researchers might be encouraged to consider conducting a similar study, allowing the findings of future SWAT-1s to be compared and combined, so that higher level evidence on the effect of a site visit by the principal investigator can be obtained.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

An Evaluation of the Drug and Alcohol Service User Network in NI

Objectives:
The process evaluation will consider the views of the appointed SUN workers and representatives from selected service user groups as regards the setting up and maintenance of the SUN network. This component of the evaluation will also examine the perceptions of stakeholders from a number of relevant organisations.

The outcome evaluation will assess the effectiveness of the SUN project in achieving the intended outcomes as outlined in the original Action Plans.
The following outcomes will be evaluated:
To ascertain the level to which the SUN has provided support, information and advice to existing service user groups.
To examine the SUN co-ordination of Trust and regional networks of service user groups.
To consider how the SUN assists organisations to establish and maintain service user groups.
To examine the level of current and future membership of service users on relevant groups, with a particular focus on engagement of hard to reach populations.
To gauge service user perceptions of the Service User Network.
To examine the levels of training provided and consider the efficacy of training.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Effectiveness Of Feature Detection Operators On The Performance Of Iris Biometric Recognition System

Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed systems, make it a good candidate to replace most of thesecurity systems around. By making use of the distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person. Identification of this person is possible by applying appropriate matching algorithm.In this paper, Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical analysis of different feature detection operators is performed, features extracted is encoded using Haar wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and False Reject Rate is 10%.