Integrated Neural Based System for State Estimation and Confidence Limit Analysis in Water Networks

In this paper a simple recurrent neural network (NN) is used as a basis for constructing an integrated system capable of finding the state estimates with corresponding confidence limits for water distribution systems. In the first phase of calculations a neural linear equations solver is combined with a Newton-Raphson iterations to find a solution to an overdetermined set of nonlinear equations describing water networks. The mathematical model of the water system is derived using measurements and pseudomeasurements consisting certain amount of uncertainty. This uncertainty has an impact on the accuracy to which the state estimates can be calculated. The second phase of calculations, using the same NN, is carried out in order to quantify the effect of measurement uncertainty on accuracy of the derived state estimates. Rather than a single deterministic state estimate, the set of all feasible states corresponding to a given level of measurement uncertainty is calculated. The set is presented in the form of upper and lower bounds for the individual variables, and hence provides limits on the potential error of each variable. The simulations have been carried out and results are presented for a realistic 34-node water distribution network

Neural Simulation of Water Systems for Efficient State Estimation

This paper presents a neural network based technique for the solution of a water system state estimation problem.The technique combines a neural linear equations solver with a Newton-Raphson iterations to obtain a solution to an overdetermined set of nonlinear equations. The algorithm has been applied to a realistic 34-node water network. By changing the values of neural network parameters both the least squares (LS) and least absolute values (LAV) estimates have been obtained and assessed with respect to their sensitivity to measurement errors

Simulation of Water Distribution Systems

In this paper a software package offering a means of simulating complex water distribution systems is described. It has been developed in the course of our investigations into the applicability of neural networks and fuzzy systems for the implementation of decision support systems in operational control of industrial processes with case-studies taken from the water industry. Examples of how the simulation package have been used in a design and testing of the algorithms for state estimation, confidence limit analysis and fault detection are presented. Arguments for using a suitable graphical visualization techniques in solving problems like meter placement or leakage diagnosis are also given and supported by a set of examples

General fuzzy min-max neural network for clustering and classification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given

FADI: a fault-tolerant environment for open distributed computing

FADI is a complete programming environment that serves the reliable execution of distributed application programs. FADI encompasses all aspects of modern fault-tolerant distributed computing. The built-in user-transparent error detection mechanism covers processor node crashes and hardware transient failures. The mechanism also integrates user-assisted error checks into the system failure model. The nucleus non-blocking checkpointing mechanism combined with a novel selective message logging technique delivers an efficient, low-overhead backup and recovery mechanism for distributed processes. FADI also provides means for remote automatic process allocation on the distributed system nodes

An approach to rollback recovery of collaborating mobile agents

Fault-tolerance is one of the main problems that must be resolved to improve the adoption of the agents' computing paradigm. In this paper, we analyse the execution model of agent platforms and the significance of the faults affecting their constituent components on the reliable execution of agent-based applications, in order to develop a pragmatic framework for agent systems fault-tolerance. The developed framework deploys a communication-pairs independent check pointing strategy to offer a low-cost, application-transparent model for reliable agent- based computing that covers all possible faults that might invalidate reliable agent execution, migration and communication and maintains the exactly-one execution property

High-precision scattering amplitudes for LHC phenomenology

In this work, we consider scattering amplitudes relevant for high-precision Large Hadron Collider (LHC) phenomenology. We analyse the general structure of amplitudes, and we review state-of-the-art methods for computing them. We discuss advantages and shortcomings of these methods, and we point out the bottlenecks in modern amplitude computations. As a practical illustration, we present frontier applications relevant for multi-loop multi-scale processes. We compute the helicity amplitudes for diphoton production in gluon fusion and photon+jet production in proton scattering in three-loop massless Quantum Chromodynamics (QCD). We have adopted a new projector-based prescription to compute helicity amplitudes in the 't Hooft-Veltman scheme. We also rederived the minimal set of independent Feynman integrals for this problem using the differential equations method, and we confirmed their intricate analytic properties. By employing modern methods for integral reduction, we provide the final results in a compact form, which is appropriate for efficient numerical evaluation. Beyond QCD, we have computed the two-loop mixed QCD-electroweak amplitudes for Z+jet production in proton scattering in light-quark-initiated channels, without closed fermion loops. This process provides important insight into the high-precision studies of the Standard Model, as well as into Dark Matter searches at the LHC. We have employed a numerical approach based on high-precision evaluation of Feynman integrals with the modern Auxiliary Mass Flow method. The obtained numerical results in all relevant partonic channels are evaluated on a two-dimensional grid appropriate for further phenomenological applications.Comment: DPhil thesis, University of Oxford: 158 pages, 52 figures, 4 tables, based on arXiv:2211.13595, arXiv:2212.06287, and arXiv:2212.1406