Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Magnetization enumerator for real-valued symmetric channels in Gallager error-correcting codes

Using the magnetization enumerator method, we evaluate the practical and theoretical limitations of symmetric channels with real outputs. Results are presented for several regular Gallager code constructions.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004.

Development of real-time process control systems using formal techniques

A major application of computers has been to control physical processes in which the computer is embedded within some large physical process and is required to control concurrent physical processes. The main difficulty with these systems is their event-driven characteristics, which complicate their modelling and analysis. Although a number of researchers in the process system community have approached the problems of modelling and analysis of such systems, there is still a lack of standardised software development formalisms for the system (controller) development, particular at early stage of the system design cycle. This research forms part of a larger research programme which is concerned with the development of real-time process-control systems in which software is used to control concurrent physical processes. The general objective of the research in this thesis is to investigate the use of formal techniques in the analysis of such systems at their early stages of development, with a particular bias towards an application to high speed machinery. Specifically, the research aims to generate a standardised software development formalism for real-time process-control systems, particularly for software controller synthesis. In this research, a graphical modelling formalism called Sequential Function Chart (SFC), a variant of Grafcet, is examined. SFC, which is defined in the international standard IEC1131 as a graphical description language, has been used widely in industry and has achieved an acceptable level of maturity and acceptance. A comparative study between SFC and Petri nets is presented in this thesis. To overcome identified inaccuracies in the SFC, a formal definition of the firing rules for SFC is given. To provide a framework in which SFC models can be analysed formally, an extended time-related Petri net model for SFC is proposed and the transformation method is defined. The SFC notation lacks a systematic way of synthesising system models from the real world systems. Thus a standardised approach to the development of real-time process control systems is required such that the system (software) functional requirements can be identified, captured, analysed. A rule-based approach and a method called system behaviour driven method (SBDM) are proposed as a development formalism for real-time process-control systems.

Automatic processing, quality assurance and serving of real-time weather data over lightweight protocols

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Automatic processing, quality assurance and serving of real-time weather data

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.

Image enhancement of real-time television to benefit the visually impaired

Purpose: To examine the use of real-time, generic edge detection, image processing techniques to enhance the television viewing of the visually impaired. Design: Prospective, clinical experimental study. Method: One hundred and two sequential visually impaired (average age 73.8 ± 14.8 years; 59% female) in a single center optimized a dynamic television image with respect to edge detection filter (Prewitt, Sobel, or the two combined), color (red, green, blue, or white), and intensity (one to 15 times) of the overlaid edges. They then rated the original television footage compared with a black-and-white image displaying the edges detected and the original television image with the detected edges overlaid in the chosen color and at the intensity selected. Footage of news, an advertisement, and the end of program credits were subjectively assessed in a random order. Results: A Prewitt filter was preferred (44%) compared with the Sobel filter (27%) or a combination of the two (28%). Green and white were equally popular for displaying the detected edges (32%), with blue (22%) and red (14%) less so. The average preferred edge intensity was 3.5 ± 1.7 times. The image-enhanced television was significantly preferred to the original (P < .001), which in turn was preferred to viewing the detected edges alone (P < .001) for each of the footage clips. Preference was not dependent on the condition causing visual impairment. Seventy percent were definitely willing to buy a set-top box that could achieve these effects for a reasonable price. Conclusions: Simple generic edge detection image enhancement options can be performed on television in real-time and significantly enhance the viewing of the visually impaired. © 2007 Elsevier Inc. All rights reserved.

Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

24-Karat or fool’s gold? Consequences of real team and co-acting group membership in healthcare organizations

Although theory on team membership is emerging, limited empirical attention has been paid to the effects of different types of team membership on outcomes. We propose that an important but overlooked distinction is that between membership of real teams and membership of co-acting groups, with the former being characterized by members who report that their teams have shared objectives, and structural interdependence and engage in team reflexivity. We hypothesize that real team membership will be associated with more positive individual- and organizational-level outcomes. These predictions were tested in the English National Health Service, using data from 62,733 respondents from 147 acute hospitals. The results revealed that individuals reporting the characteristics of real team membership, in comparison with those reporting the characteristics of co-acting group membership, witnessed fewer errors and incidents, experienced fewer work related injuries and illness, were less likely to be victims of violence and harassment, and were less likely to intend to leave their current employment. At the organizational level, hospitals with higher proportions of staff reporting the characteristics of real team membership had lower levels of patient mortality and sickness absence. The results suggest the need to clearly delineate real team membership in order to advance scientific understanding of the processes and outcomes of organizational teamwork.

Mixture density networks

Minimization of a sum-of-squares or cross-entropy error function leads to network outputs which approximate the conditional averages of the target data, conditioned on the input vector. For classifications problems, with a suitably chosen target coding scheme, these averages represent the posterior probabilities of class membership, and so can be regarded as optimal. For problems involving the prediction of continuous variables, however, the conditional averages provide only a very limited description of the properties of the target variables. This is particularly true for problems in which the mapping to be learned is multi-valued, as often arises in the solution of inverse problems, since the average of several correct target values is not necessarily itself a correct value. In order to obtain a complete description of the data, for the purposes of predicting the outputs corresponding to new input vectors, we must model the conditional probability distribution of the target data, again conditioned on the input vector. In this paper we introduce a new class of network models obtained by combining a conventional neural network with a mixture density model. The complete system is called a Mixture Density Network, and can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. We demonstrate the effectiveness of Mixture Density Networks using both a toy problem and a problem involving robot inverse kinematics.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.