Noise due to surface wind and temperature is a problem in infrasound. Efficiency of IMS network concerns scientists. It is obvious to find the causes of deficiencies of detection of infrasound station by studying back...Noise due to surface wind and temperature is a problem in infrasound. Efficiency of IMS network concerns scientists. It is obvious to find the causes of deficiencies of detection of infrasound station by studying background noise power with respect to the surface wind and the temperature. Data measured by MB2000 microbarometer of infrasound station I33MG are used for the study. Infrasound records are separated into 4 frequency bands centered respectively at: 1 Hz, 0.25 Hz, 0.0625 Hz and 0.0156 Hz. Effects of surface wind and temperature are studied by plotting the variations of the background noise power with respect to the temperature or wind speed in the four considered frequency bands and compared with the median of background noise power. The influence of temperature is manifested by a reduction in the number of low-frequency detection. The surface wind reduces the number of detection at a high frequency. An exponential function is proposed to predict the variations of the noise power in different observation frequencies and temperature and wind conditions. The views expressed herein are those of the authors and do not necessarily reflect the views of the CTBTO Preparatory Commission.展开更多
We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the pow...We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms.A comparison between the power curve models based on artificial neural networks(ANNs)and those based on fuzzy logic are also proposed.Some of the power curve models based on ANNs and fuzzy inference systems(FISs)are used as well as two new FISs with the proposed new heuristic.An initial pre-training is proposed,resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system.Although the presented values by the error indicators are comparable,the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models.The comparative study is conducted in two wind farms located in northeastern Brazil.The proposed method is a relevant alternative to improve power curve approximation based on an FIS.展开更多
Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies....Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies.However,very few alignment systems focus on finding complex correspondences.One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships.In this paper,we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark.The data set consists of two ontologies,the GeoLink Base Ontology(GBO)and the GeoLink Modular Ontology(GMO),as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions.The alignment includes 1:1,1:n,and m:n equivalence and subsumption correspondences,and is available in both Expressive and Declarative Ontology Alignment Language(EDOAL)and rule syntax.The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies.This allows it to be used by extensional alignment systems or those that require training data.This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative(OAEI)complex track to help researchers test their automated alignment systems and algorithms.This paper also analyzes the challenges inherent in effectively generating,detecting,and evaluating complex ontology alignments and provides a road map for future work on this topic.展开更多
文摘Noise due to surface wind and temperature is a problem in infrasound. Efficiency of IMS network concerns scientists. It is obvious to find the causes of deficiencies of detection of infrasound station by studying background noise power with respect to the surface wind and the temperature. Data measured by MB2000 microbarometer of infrasound station I33MG are used for the study. Infrasound records are separated into 4 frequency bands centered respectively at: 1 Hz, 0.25 Hz, 0.0625 Hz and 0.0156 Hz. Effects of surface wind and temperature are studied by plotting the variations of the background noise power with respect to the temperature or wind speed in the four considered frequency bands and compared with the median of background noise power. The influence of temperature is manifested by a reduction in the number of low-frequency detection. The surface wind reduces the number of detection at a high frequency. An exponential function is proposed to predict the variations of the noise power in different observation frequencies and temperature and wind conditions. The views expressed herein are those of the authors and do not necessarily reflect the views of the CTBTO Preparatory Commission.
文摘We propose a new way to develop non-parametric models of power curves using artificial intelligence tools.One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms.A comparison between the power curve models based on artificial neural networks(ANNs)and those based on fuzzy logic are also proposed.Some of the power curve models based on ANNs and fuzzy inference systems(FISs)are used as well as two new FISs with the proposed new heuristic.An initial pre-training is proposed,resulting from the characteristics derived from the expert inference followed by a transformation of a fuzzy Mamdani system into a fuzzy Sugeno system.Although the presented values by the error indicators are comparable,the results show that the new pre-trained FIS models have better precision compared with the ANN and FIS models.The comparative study is conducted in two wind farms located in northeastern Brazil.The proposed method is a relevant alternative to improve power curve approximation based on an FIS.
文摘Ontology alignment has been studied for over a decade,and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies.However,very few alignment systems focus on finding complex correspondences.One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships.In this paper,we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark.The data set consists of two ontologies,the GeoLink Base Ontology(GBO)and the GeoLink Modular Ontology(GMO),as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions.The alignment includes 1:1,1:n,and m:n equivalence and subsumption correspondences,and is available in both Expressive and Declarative Ontology Alignment Language(EDOAL)and rule syntax.The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies.This allows it to be used by extensional alignment systems or those that require training data.This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative(OAEI)complex track to help researchers test their automated alignment systems and algorithms.This paper also analyzes the challenges inherent in effectively generating,detecting,and evaluating complex ontology alignments and provides a road map for future work on this topic.
