The acquisition of environmental data, like pollution and/or meteorological data requires the processing of a huge amount of heterogeneous data from external fields. As the number of monitoring points grows, we need a strategy to validate the acquired data and to efficiently utilize the transmission resources. An efficient way to obtain the validation-compression of the data sets is the adoption of a restricted set of samples (templates) that describe, with an assigned accuracy the whole data set. The aim of the work is to propose a validation-compression technique based on features, extracted by means of an unsupervised neural network. The paper reports the results obtained utilizing the above procedure to a real data set of a chemical pollutant. It is shown that the validation process allows a correct identification of corrupted and/or anomalous data, comparable with the human validation. Moreover the process allows a considerable reduction of transmitted data as the compression process profits the local processing of redundant data

Redundancy reduction in environmental data set by means of an unsupervised neural networks

E. Chiarantoni;G. Fornarelli;S. Vergura
2002

Abstract

The acquisition of environmental data, like pollution and/or meteorological data requires the processing of a huge amount of heterogeneous data from external fields. As the number of monitoring points grows, we need a strategy to validate the acquired data and to efficiently utilize the transmission resources. An efficient way to obtain the validation-compression of the data sets is the adoption of a restricted set of samples (templates) that describe, with an assigned accuracy the whole data set. The aim of the work is to propose a validation-compression technique based on features, extracted by means of an unsupervised neural network. The paper reports the results obtained utilizing the above procedure to a real data set of a chemical pollutant. It is shown that the validation process allows a correct identification of corrupted and/or anomalous data, comparable with the human validation. Moreover the process allows a considerable reduction of transmitted data as the compression process profits the local processing of redundant data
International Joint Conference on Neural Networks, IJCNN 02
0-7803-7278-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/20692
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