Application of neural networks trained with multizone models for fast detection of contaminant source position in buildings

ASHRAE Transactions

Volumn 113 PART 2, Issue , 2007, Pages 154-162

  Vukovic, Vladimir ^a,b   Srebric, Jelena ^a,b  

^a ASHRAE ^*

^b The Pennsylvania State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMERICAN SOCIETY; ANNUAL MEETINGS; COMPLEX APPLICATIONS; COMPUTATIONAL ARCHITECTURES; COMPUTED TOMOGRAPHY (CT); CONTAMINANT CONCENTRATIONS; CONTAMINANT DISPERSIONS; CONTAMINANT RELEASE; CONTAMINANT SOURCES; DISPERSION MODEL (DM); IN BUILDINGS; MEASURING TECHNIQUES; MULTI-ZONE MODELS; NON LINEAR MAPPINGS; NOVE L APPROACH; OF SENSORS; PRACTICAL APPLICATIONS; PREDICTION METHODS; REAL TIME; REAL-TIME DETECTION; SENSOR OPTIMIZATION; SOURCE LOCATIONS; SOURCE POSITION; STATISTICAL TECHNIQUES;

AEROSPACE APPLICATIONS; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; COMPUTATIONAL FLUID DYNAMICS; COMPUTATIONAL METHODS; COMPUTER NETWORKS; COMPUTERIZED TOMOGRAPHY; DETECTORS; DIAGNOSTIC RADIOGRAPHY; DISPERSION (WAVES); DYNAMIC PROGRAMMING; DYNAMICS; EVOLUTIONARY ALGORITHMS; FLUID DYNAMICS; FLUID MECHANICS; FLUIDICS; FORECASTING; GENETIC ALGORITHMS; HEATING; IMAGE CLASSIFICATION; IMAGING TECHNIQUES; INTEGER PROGRAMMING; LEAKAGE (FLUID); LOCATION; MATHEMATICAL MODELS; MEDICAL IMAGING; METROPOLITAN AREA NETWORKS; NETWORK ARCHITECTURE; NETWORK PROTOCOLS; NUMERICAL METHODS; SCHEDULING ALGORITHMS; SENSORS; STATISTICAL METHODS; SUPERCOMPUTERS; TOMOGRAPHY; VEGETATION;

NEURAL NETWORKS;

EID: 45749127106     PISSN: 00012505     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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