Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic infe...Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.展开更多
Global ecological degradation is a matter of enormous concern. In the early 20 st century, the United States, Europe and China began to apply eco-technology to ecosystem management and restoration in order to slow dow...Global ecological degradation is a matter of enormous concern. In the early 20 st century, the United States, Europe and China began to apply eco-technology to ecosystem management and restoration in order to slow down or stop ecological degradation. To date, there has been neither a systematic summary and scientific evaluation, nor is there a unified platform to describe ecological degradation problems in different areas and existing eco-technologies. These shortcomings have hindered the popularization and application of technologies. This study intends to build an eco-technology evaluation platform and integration system that brings together heterogeneous data from multiple sources. The key technology of the eco-technology evaluation platform and integration system is information integration technology. We will establish a metadata engine based on metadata storage to achieve access to and integration of metadata and heterogeneous data sources. The information integration mode based on a metamodel addresses information heterogeneity at four levels： system, syntax, structure and semantics. We develop the framework for an eco-technology evaluation platform and integration system to integrate ecotechnology databases, eco-technology evaluation model databases, eco-technology evaluation parameter databases and spatial databases of ecological degradation and eco-technology with metadata and metamodel integration mode. This system can support functions for the query and display of global and typical ecological degradation and the query, display, evaluation and prioritization of eco-technologies, which can realize the visualization of global and Chinese ecological degradation and eco-technology evaluation and prioritization. This system will help government decision makers and relevant departments to understand ecological degradation and the effects of ecotechnology implementation.展开更多
Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-...Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution （PSD）. However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1） to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2） to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model （VG）, was used. Through using Mohammadi and Vanclooster （MV） model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content （Or） could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating 0r. Results showed that the proposed equation （MV-VG integrated model） provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which Or Was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating Or with Campbell and Shiozawa （CS） model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of 0r was assumed to equal zero. 0r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of 0r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.展开更多
Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a n...Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.展开更多
基金Supported by National Natural Science Foundation of China(No.61601039)financially supported by the State Key Research Development Program of China(Grant No.2016YFC0801407)+3 种基金financially supported by the Natural Science Foundation of Beijing Information Science & Technology University(No.1625008)financially supported by the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(NO.ICDD201607)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(NO.SKLNST-2016-2-08)financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(Grant No.CIT&TCD201504056)
文摘Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.
基金National Key Research and Development Program of China(2016YFC0503706,2016YFC0503403)
文摘Global ecological degradation is a matter of enormous concern. In the early 20 st century, the United States, Europe and China began to apply eco-technology to ecosystem management and restoration in order to slow down or stop ecological degradation. To date, there has been neither a systematic summary and scientific evaluation, nor is there a unified platform to describe ecological degradation problems in different areas and existing eco-technologies. These shortcomings have hindered the popularization and application of technologies. This study intends to build an eco-technology evaluation platform and integration system that brings together heterogeneous data from multiple sources. The key technology of the eco-technology evaluation platform and integration system is information integration technology. We will establish a metadata engine based on metadata storage to achieve access to and integration of metadata and heterogeneous data sources. The information integration mode based on a metamodel addresses information heterogeneity at four levels： system, syntax, structure and semantics. We develop the framework for an eco-technology evaluation platform and integration system to integrate ecotechnology databases, eco-technology evaluation model databases, eco-technology evaluation parameter databases and spatial databases of ecological degradation and eco-technology with metadata and metamodel integration mode. This system can support functions for the query and display of global and typical ecological degradation and the query, display, evaluation and prioritization of eco-technologies, which can realize the visualization of global and Chinese ecological degradation and eco-technology evaluation and prioritization. This system will help government decision makers and relevant departments to understand ecological degradation and the effects of ecotechnology implementation.
文摘Soil moisture characteristic curve （SMC） is a fundamental soil property and its direct measurement is tedious and time consuming. Therefore, various indirect methods have been developed to predict SMC from particle-size distribution （PSD）. However, the majority of these methods often yield intermittent SMC data because they involve estimating individual SMC points. The objectives of this study were 1） to develop a procedure to predict continuous SMC from a limited number of experimental PSD data points and 2） to evaluate model predictions through comparisons with measured values. In this study, an approach that allowed predicting SMC from the knowledge of PSD, parameterized by means of the closed-form van Genuchten model （VG）, was used. Through using Mohammadi and Vanclooster （MV） model, the parameters obtained from fitting of VG to PSD data were applied to predict SMC curves. Since the residual water content （Or） could not be obtained through fitting of VG-MV integrated model to PSD data, we also examined and compared four different methods estimating 0r. Results showed that the proposed equation （MV-VG integrated model） provided an excellent fit to all the PSD data and the model could adequately predict SMC as measured in forty-two soils sampled from different regions of Iran. For all soils, the method in which Or Was obtained through parameter optimization procedure provided the best overall predictions of SMC. The two methods estimating Or with Campbell and Shiozawa （CS） model resulted in less accuracy than the optimization procedure. Furthermore, the proposed model underestimated the moisture content in the dry range of SMC when the value of 0r was assumed to equal zero. 0r could be attributed to the incomplete desorption of water coated on soil particles and the accurate estimation of 0r was critical in prediction of SMC, especially for fine-textured soils at high suction heads. It could be concluded that the advantages of our approach were the continuity, robustness, and independency of model performance on soil type, allowing to improve predictions of SMC from PSD at the field and watershed scales.
基金Project supported by the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW) of South Africa (No.GW51/072)the National Research Foundation (NRF) of South Africa (No.GW 51/083/01)the Water Research Commission (WRC)of South Africa (No.K5/1849)
文摘Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.
