Issues on intelligent resource description and multiple intelligent resources integration for lntemet based collaborative design are analyzed. A performance-based intelligent resource description model for lnternet-ba...Issues on intelligent resource description and multiple intelligent resources integration for lntemet based collaborative design are analyzed. A performance-based intelligent resource description model for lnternet-based product design is proposed, which can help to create, store, manipulate and exchange intelligent resource description information for applications, tools and systems in Interact-based product design. A method to integrate multiple intelligent resources to fulfill a complex product design and analysis via lntemet is also proposed. A real project for improving the bearing system design of a turbo-expander with many intelligent resources in prominent universities is presented as a case study.展开更多
The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location ...The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.展开更多
基金This project is supported by National Natural Science Foundation of China (No.59990472)Doctor Foundation of Ministry of Education of China (No.20030698005, No.20050698016).
文摘Issues on intelligent resource description and multiple intelligent resources integration for lntemet based collaborative design are analyzed. A performance-based intelligent resource description model for lnternet-based product design is proposed, which can help to create, store, manipulate and exchange intelligent resource description information for applications, tools and systems in Interact-based product design. A method to integrate multiple intelligent resources to fulfill a complex product design and analysis via lntemet is also proposed. A real project for improving the bearing system design of a turbo-expander with many intelligent resources in prominent universities is presented as a case study.
基金supported by the Joint Civil Aviation Fund of National Natural Science Foundation of China(Nos.U1533108 and U1233112)
文摘The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.
