In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the r...In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.展开更多
The application collaboration was addressed to provide energy-efficient data services for distributed sensing applications to collaboratively interacting to achieve a desired global objective not detectable by any sin...The application collaboration was addressed to provide energy-efficient data services for distributed sensing applications to collaboratively interacting to achieve a desired global objective not detectable by any single cluster. An epoch-based transaction model was proposed by using the concept of sphere of control （SoC）, and a collaborative sensing application can be dynamically formed as a nested architecture composed of time-synchronized applications. By loosening the rigid constraints of ACID to adapt the requirements of sensor networks, the submission, rollback and consistency rules ware educed and a two-phrase submission protocol was designed. Finally, it was illustrated that the model is capable of providing an adaptive formal template for sensing application collaboration. Our performance evaluations show that by applying the two-phrase submission protocol, we can significantly improve the number of reported answers and response time, raise resource utilization, and reduce the energy cansumption and data loss.展开更多
This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Corr...This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction （LRC） is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm （AGA）. The experiments showed that this method can monitor red tide rapidly and effectively.展开更多
基金Supported by the National Natural Science Foundation of China(61622301,61533002)Beijing Natural Science Foundation(4172005)Major National Science and Technology Project(2017ZX07104)
文摘In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.
基金National Natural Science Foundation of China under Grant No60073045the National Defense Pre-Research Foundation of China under Grant No.00J15.3.3.J W529
文摘The application collaboration was addressed to provide energy-efficient data services for distributed sensing applications to collaboratively interacting to achieve a desired global objective not detectable by any single cluster. An epoch-based transaction model was proposed by using the concept of sphere of control （SoC）, and a collaborative sensing application can be dynamically formed as a nested architecture composed of time-synchronized applications. By loosening the rigid constraints of ACID to adapt the requirements of sensor networks, the submission, rollback and consistency rules ware educed and a two-phrase submission protocol was designed. Finally, it was illustrated that the model is capable of providing an adaptive formal template for sensing application collaboration. Our performance evaluations show that by applying the two-phrase submission protocol, we can significantly improve the number of reported answers and response time, raise resource utilization, and reduce the energy cansumption and data loss.
基金This research was fully supported by the National 863 Natural Science Foundation of P.R.China(2001 AA636030).
文摘This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction （LRC） is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm （AGA）. The experiments showed that this method can monitor red tide rapidly and effectively.
