In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance...In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.展开更多
For the release of hazardous contaminant indoors, source identification is critical for developing effective response measures. A method which can quickly and accurately identify the position, emission rate, and relea...For the release of hazardous contaminant indoors, source identification is critical for developing effective response measures. A method which can quickly and accurately identify the position, emission rate, and release time of a single constant contaminant source by using real sensors was presented. The method was numerically demonstrated and validated by a case study of contaminant release in a three-dimensional office. The effects of the measurement errors and total sampling period of sensor on the performance of source identification were thoroughly studied. The results indicate that the adverse effects of the measurement errors can be mitigated by extending the total sampling period. For reaching a desirable accuracy of source identification, the total sampling period should exceed a certain threshold, which can be determined by repeatedly running the identification method tmtil the results tend to be stable. The method presented can contribute to develop an onsite source identification system for protecting occupants from indoor releases.展开更多
A new scheduling algorithm called deferrable scheduling with time slice exchange (DS-EXC) was proposed to maintain the temporal validity of real-time data. In DS-EXC, the time slice exchange method was designed to fur...A new scheduling algorithm called deferrable scheduling with time slice exchange (DS-EXC) was proposed to maintain the temporal validity of real-time data. In DS-EXC, the time slice exchange method was designed to further defer the release time of transaction instances derived by the deferrable scheduling algorithm (DS-FP). In this way, more CPU time would be left for lower priority transactions and other transactions. In order to minimize the scheduling overhead, an off-line scheme was designed. In particular, the schedule for a transaction set is generated off-line until a repeating pattern is found, and then the pattern is used to construct the schedule on-line. The performance of DS-EXC was evaluated by sets of experiments. The results show that DS-EXC outperforms DS-FP in terms of increasing schedulable ratio. It also provides better performance under mixed workloads.展开更多
文摘In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.
基金Project(50908128) supported by the National Natural Science Foundation of ChinaProject(51125030) supported by the National Science Foundation for Distinguished Young Scholars in China
文摘For the release of hazardous contaminant indoors, source identification is critical for developing effective response measures. A method which can quickly and accurately identify the position, emission rate, and release time of a single constant contaminant source by using real sensors was presented. The method was numerically demonstrated and validated by a case study of contaminant release in a three-dimensional office. The effects of the measurement errors and total sampling period of sensor on the performance of source identification were thoroughly studied. The results indicate that the adverse effects of the measurement errors can be mitigated by extending the total sampling period. For reaching a desirable accuracy of source identification, the total sampling period should exceed a certain threshold, which can be determined by repeatedly running the identification method tmtil the results tend to be stable. The method presented can contribute to develop an onsite source identification system for protecting occupants from indoor releases.
基金Project(60873030) supported by the National Natural Science Foundation of China
文摘A new scheduling algorithm called deferrable scheduling with time slice exchange (DS-EXC) was proposed to maintain the temporal validity of real-time data. In DS-EXC, the time slice exchange method was designed to further defer the release time of transaction instances derived by the deferrable scheduling algorithm (DS-FP). In this way, more CPU time would be left for lower priority transactions and other transactions. In order to minimize the scheduling overhead, an off-line scheme was designed. In particular, the schedule for a transaction set is generated off-line until a repeating pattern is found, and then the pattern is used to construct the schedule on-line. The performance of DS-EXC was evaluated by sets of experiments. The results show that DS-EXC outperforms DS-FP in terms of increasing schedulable ratio. It also provides better performance under mixed workloads.
