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 / Chiarantoni, E.; Fornarelli, G.; Vergura, S.. - STAMPA. - (2002), pp. 412-416. (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 02 tenutosi a Honolulu, HI nel May 12-17, 2002) [10.1109/IJCNN.2002.1005507].

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

E. Chiarantoni;G. Fornarelli;S. Vergura
2002-01-01

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
2002
International Joint Conference on Neural Networks, IJCNN 02
0-7803-7278-6
Redundancy reduction in environmental data set by means of an unsupervised neural networks / Chiarantoni, E.; Fornarelli, G.; Vergura, S.. - STAMPA. - (2002), pp. 412-416. (Intervento presentato al convegno International Joint Conference on Neural Networks, IJCNN 02 tenutosi a Honolulu, HI nel May 12-17, 2002) [10.1109/IJCNN.2002.1005507].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/20692
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