메뉴 건너뛰기




Volumn 4, Issue 3, 2003, Pages 217-229

Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system

Author keywords

Hydraulic sensor; Leakage detection; Neural network; Rules based fusion; Water distribution network

Indexed keywords

HYDRAULICS; LEAKAGE (FLUID); NEURAL NETWORKS; SENSORS; WATER PIPELINES;

EID: 0042329657     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1566-2535(03)00034-4     Document Type: Article
Times cited : (92)

References (36)
  • 1
    • 0042665276 scopus 로고    scopus 로고
    • OFWAT, Leakage figures. Available from www.ofwat.gov.uk, 2001.
    • (2001) Leakage Figures
  • 4
    • 0012722022 scopus 로고
    • Leakage control policy practice
    • DWI0190, London, UK
    • Department of the Environment and the National Water Council, Leakage Control Policy and Practice, Technical committee report No. 26, DWI0190, London, UK, 1980.
    • (1980) Technical Committee Report No. 26 , vol.26
  • 6
    • 0043166246 scopus 로고    scopus 로고
    • Find that leak
    • Woodward G.T. Find that leak. IEE Review. 1999;219-221.
    • (1999) IEE Review , pp. 219-221
    • Woodward, G.T.1
  • 7
    • 0005049156 scopus 로고    scopus 로고
    • Practical applications of real time hydraulic modelling of water distribution networks
    • Skipworth P.J., Saul A., Machell J. Practical applications of real time hydraulic modelling of water distribution networks. Int. J. Comadem. 2(1):1999;15-21.
    • (1999) Int. J. Comadem , vol.2 , Issue.1 , pp. 15-21
    • Skipworth, P.J.1    Saul, A.2    Machell, J.3
  • 9
    • 0014700838 scopus 로고
    • Power system static state estimation
    • Schweppe F.C. Power system static state estimation. IEEE Trans. PAS. 89(1):1970;120-135.
    • (1970) IEEE Trans. PAS , vol.89 , Issue.1 , pp. 120-135
    • Schweppe, F.C.1
  • 10
    • 0021386121 scopus 로고
    • Minimum norm state estimation for computer control of water distribution systems
    • Sterling M., Bargiela A. Minimum norm state estimation for computer control of water distribution systems. Procs. IEE. 131D(2):1984;57-63.
    • (1984) Procs. IEE , vol.131 D , Issue.2 , pp. 57-63
    • Sterling, M.1    Bargiela, A.2
  • 11
    • 0003336180 scopus 로고
    • A comparison of three real-time state-estimation methods for online monitoring of water distribution systems
    • Coulbeck B. Research Studies Press
    • Powell R.S., Irving M.R., Sterling M.J.H. A comparison of three real-time state-estimation methods for online monitoring of water distribution systems. Coulbeck B. Computer Applications in Water Supply. vol. 1:1988;333-348 Research Studies Press.
    • (1988) Computer Applications in Water Supply , vol.1 , pp. 333-348
    • Powell, R.S.1    Irving, M.R.2    Sterling, M.J.H.3
  • 12
    • 0035383832 scopus 로고    scopus 로고
    • Constrained state estimation with applications in water distribution network monitoring
    • Andersen J.H., Powell R.S., Marsh J.F. Constrained state estimation with applications in water distribution network monitoring. Int. J. Syst. Sci. 32(6):2001;807-816.
    • (2001) Int. J. Syst. Sci. , vol.32 , Issue.6 , pp. 807-816
    • Andersen, J.H.1    Powell, R.S.2    Marsh, J.F.3
  • 13
    • 0040362346 scopus 로고    scopus 로고
    • Implicit state-estimation technique for water network monitoring
    • Andersen J.H., Powell R.S. Implicit state-estimation technique for water network monitoring. Urban Water. 2(2):2000;123-130.
    • (2000) Urban Water , vol.2 , Issue.2 , pp. 123-130
    • Andersen, J.H.1    Powell, R.S.2
  • 14
    • 0028484821 scopus 로고
    • Inverse transient analysis in pipe networks
    • Liggett J.A., Chen L.C. Inverse transient analysis in pipe networks. J. Hydraul. Eng. - ASCE. 120(8):1994;934-955.
    • (1994) J. Hydraul. Eng. - ASCE , vol.120 , Issue.8 , pp. 934-955
    • Liggett, J.A.1    Chen, L.C.2
  • 16
    • 0000779360 scopus 로고
    • Detecting strange attractors in fluid turbulence
    • D. Rand, & L. Young. Berlin: Springer
    • Takens F. Detecting strange attractors in fluid turbulence. Rand D., Young L. Dynamical Systems and Turbulence. 1981;366-381 Springer, Berlin.
    • (1981) Dynamical Systems and Turbulence , pp. 366-381
    • Takens, F.1
  • 17
    • 33646981873 scopus 로고
    • Characterisation of strange attractors
    • Grassberger P., Procaccia I. Characterisation of strange attractors. Phys. Rev. Lett. 50(5):1983;346-349.
    • (1983) Phys. Rev. Lett. , vol.50 , Issue.5 , pp. 346-349
    • Grassberger, P.1    Procaccia, I.2
  • 18
    • 0000706678 scopus 로고
    • Using backpropagation with temporal windows to learn the dynamics of CMU direct-drive arm
    • Goldberg K.Y., Pearlmutter B.A. Using backpropagation with temporal windows to learn the dynamics of CMU direct-drive arm. Adv. Neural Informat. Process. Syst. I:1989;356-363.
    • (1989) Adv. Neural Informat. Process. Syst. , vol.1 , pp. 356-363
    • Goldberg, K.Y.1    Pearlmutter, B.A.2
  • 19
    • 26444565569 scopus 로고
    • Finding structures in time
    • Elman J.L. Finding structures in time. Cognitive Sci. 14:1990;179-211.
    • (1990) Cognitive Sci. , vol.14 , pp. 179-211
    • Elman, J.L.1
  • 21
    • 0002291616 scopus 로고
    • Neural net architecture for temporal sequence processing
    • A.S. Weigend, & N.A. Gershenfeld. Redwood City, CA: Addison-Wesley
    • Mozer M.C. Neural net architecture for temporal sequence processing. Weigend A.S., Gershenfeld N.A. Time Series Prediction: Predicting the Future and Understanding the Past. 1994;243-264 Addison-Wesley, Redwood City, CA.
    • (1994) Time Series Prediction: Predicting the Future and Understanding the Past , pp. 243-264
    • Mozer, M.C.1
  • 22
    • 0042665272 scopus 로고    scopus 로고
    • Forecasting fetal heartbeats with neural networks
    • A.B. et al. Bulsari. Turku: Systeemitekniikan seura ry
    • Ulbricht C., Dorffner G., Lee A. Forecasting fetal heartbeats with neural networks. Bulsari A.B.et al. Solving Engineering Problems with Neural Networks. 1996;403-406 Systeemitekniikan seura ry, Turku.
    • (1996) Solving Engineering Problems with Neural Networks , pp. 403-406
    • Ulbricht, C.1    Dorffner, G.2    Lee, A.3
  • 24
    • 0032780932 scopus 로고    scopus 로고
    • Predicting water quality in distribution using artificial neural networks
    • Skipworth P.J., Saul A., Machell J. Predicting water quality in distribution using artificial neural networks. ICE J., Water Marit. Energy. 136(1):1999;1-8.
    • (1999) ICE J., Water Marit. Energy , vol.136 , Issue.1 , pp. 1-8
    • Skipworth, P.J.1    Saul, A.2    Machell, J.3
  • 25
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornick K., Maxwell S., Halbert W. Multilayer feedforward networks are universal approximators. Neural Networks. 2:1989;359-366.
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornick, K.1    Maxwell, S.2    Halbert, W.3
  • 26
    • 0029313018 scopus 로고
    • Predicting conditional probability distribution: A connectionist approach
    • Weigend A.S., Srivastava A.N. Predicting conditional probability distribution: A connectionist approach. Int. J. Neural Syst. 6(2):1995;109-118.
    • (1995) Int. J. Neural Syst. , vol.6 , Issue.2 , pp. 109-118
    • Weigend, A.S.1    Srivastava, A.N.2
  • 28
    • 0002245041 scopus 로고
    • Estimation of conditional densities: A comparison of neural network approaches
    • M. Marinaro, & P. Morasso. Berlin: Springer
    • Neuneier R., Hergert F., Finnoff W., Ormoneit D. Estimation of conditional densities: A comparison of neural network approaches. Marinaro M., Morasso P. Proceedings of ICANN'94. 1994;689-692 Springer, Berlin.
    • (1994) Proceedings of ICANN'94 , pp. 689-692
    • Neuneier, R.1    Hergert, F.2    Finnoff, W.3    Ormoneit, D.4
  • 29
    • 0031913824 scopus 로고    scopus 로고
    • Neural networks for predicting conditional probability densities: Improved training scheme combining EM and RVFL
    • Husmeier D., Taylor J.G. Neural networks for predicting conditional probability densities: Improved training scheme combining EM and RVFL. Neural Networks. 11(1):1998;89-116.
    • (1998) Neural Networks , vol.11 , Issue.1 , pp. 89-116
    • Husmeier, D.1    Taylor, J.G.2
  • 30
    • 0002245041 scopus 로고
    • Estimation of conditional densities: A comparison of neural network approaches
    • NETLAB Toolbox, based on techniques of. M. Marinaro, & P. Morasso. Berlin: Springer. (Department of Computer Science and Applied Mathematics, Aston University, Birmingham, UK)
    • NETLAB Toolbox, based on techniques of Neuneier R., Hergert F., Finnoff W., Ormoneit D. Estimation of conditional densities: A comparison of neural network approaches. Marinaro M., Morasso P. Proceedings of ICANN'94. 1994;689-692 Springer, Berlin. (Department of Computer Science and Applied Mathematics, Aston University, Birmingham, UK).
    • (1994) Proceedings of ICANN'94 , pp. 689-692
    • Neuneier, R.1    Hergert, F.2    Finnoff, W.3    Ormoneit, D.4
  • 33
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • Moller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks. 6(4):1993;525-533.
    • (1993) Neural Networks , vol.6 , Issue.4 , pp. 525-533
    • Moller, M.1
  • 35
    • 0036204309 scopus 로고    scopus 로고
    • Low-cost failure sensor design and development for water pipeline distribution systems
    • Khan A., Widdop P.D., Day A.J., Wood A.S., Mounce S.R., Machell J. Low-cost failure sensor design and development for water pipeline distribution systems. Water Sci. Technol. (IWA). 45(4-5):2002;207-216.
    • (2002) Water Sci. Technol. (IWA) , vol.45 , Issue.4-5 , pp. 207-216
    • Khan, A.1    Widdop, P.D.2    Day, A.J.3    Wood, A.S.4    Mounce, S.R.5    Machell, J.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.