Calculation of Leakage in Water Supply Network Based on Blind Source Separation Theory
Authors
Abstract:
The economic and environmental losses due to serious leakage in the urban water supply network have increased the effort to control the water leakage. However, current methods for leakage estimation are inaccurate leading to the development of ineffective leakage controls. Therefore, this study proposes a method based on the blind source separation theory (BSS) to calculate the leakage of water supply network. The method uses fast independent component analysis (FastICA) algorithm to separate flow signal of laboratory and practical measuring area, adopts trend similarity to solve the uncertainty of separation sequence to get hourly change curve of user usage and physical leakage, and embeds the leakage model into amplitude optimization model to solve amplitude uncertainty to obtain physical leakage value. The study found that the estimation of leakage level using the blind source separation is reasonably accurate and facilitates the identification of the subsequent reduction in water leakage. This can provide scientific evidence for leakage reduction and the investment of pressure relief devices in the next stage.
similar resources
Monitoring abnormal network traffic based on blind source separation approach
The randomness in network behaviors poses serious challenges for discovering abnormal patterns in network traffic flows. This paper presents a systematic approach for monitoring abnormal network traffic. The DFlow model is proposed to reduce the flow records and extract four features to capture the traffic patterns. The blind source separation method is applied to obtain the routine and abnorma...
full textBlind source separation based on self-organizing neural network
This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required about the statistical properties of the sources and the coefficients of the linear transformation, except the fact that the source signals are statistically independent and nonstationary. This is often true for real life a...
full textNeural Network Based Blind Source Separation of Non-linear Mixtures
In this paper we present a novel neural network topology capable of separating simultaneous signals transferred through a memoryless non-linear path. The employed neural network is a two-layer perceptron that uses parametric non-linearities in the hidden neurons. The non-linearities are formed using a mixture of sigmoidal non-linear functions and present greater adaptation towards separating co...
full textBlind source separation using algorithmic information theory
Previous approaches for the blind source separation problem have used independent component analysis making the separated components statistically independent. In this paper, a new contrast for blind source separation of natural signals is proposed, which measures the algorithmic complexity of the sources and also the complexity of the mixing mapping. No assumptions about underlying probability...
full textFast blind separation based on information theory
Blind separation is an information theoretic problem , and we have proposed an information theoretic`sigmoid-based' solution 2]. Here we elaborate on several aspects of that solution. Firstly, we argue that the separation matrix may be exactly found by maximis-ing the joint entropy of the random vector resulting from a linear transformation of the mixtures followed by sigmoidal non-linearities ...
full textComparison of Blind Source Separation Methods based on Iterative Algorithms
In this paper some approaches to detect signal streams of a multi layer transmission system are presented. We will focus on blind algorithms for the separation of the data stream and improve their performance in an iterative way in order to gain nearly the same performance as with a known channel matrix. The overall algorithm will remain blind and does not need any training data.
full textMy Resources
Journal title
volume 2 issue 1
pages 58- 70
publication date 2016-08-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023