We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly con...We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.展开更多
The time optimal problem for a two level quantum sys-tem is studied. We compare two different control strategies of bang-bang control and the geometric control, respectively, es-pecial y in the case of minimizing the ...The time optimal problem for a two level quantum sys-tem is studied. We compare two different control strategies of bang-bang control and the geometric control, respectively, es-pecial y in the case of minimizing the time of steering the state from North Pole to South Pole on the Bloch sphere with bounded control. The time performances are compared for different param-eters by the individual numerical simulation experiments, and the experimental results are analyzed. The results show that the ge-ometric control spends less time than the bang-bang control does.展开更多
Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile deposition...Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels.Three cored wellbores drilled through such a reservoir in a large oil field,with just four recorded well logs available,are used to classify four lithofacies using ML models.To augment the well-log data,six derivative and volatility attributes were calculated from the recorded gamma ray and density logs,providing sixteen log features for the ML models to select from.A novel,multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation.Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation.When the trained ML models were applied to a third well for testing,lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features.However,an accuracy of~0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well.A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with~0.6 accuracy.Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.展开更多
文摘We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.
基金supported by the National Key Basic Research Program(2011CBA00200)the National Natural Science Foundation of China(61074050)
文摘The time optimal problem for a two level quantum sys-tem is studied. We compare two different control strategies of bang-bang control and the geometric control, respectively, es-pecial y in the case of minimizing the time of steering the state from North Pole to South Pole on the Bloch sphere with bounded control. The time performances are compared for different param-eters by the individual numerical simulation experiments, and the experimental results are analyzed. The results show that the ge-ometric control spends less time than the bang-bang control does.
文摘Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels.Three cored wellbores drilled through such a reservoir in a large oil field,with just four recorded well logs available,are used to classify four lithofacies using ML models.To augment the well-log data,six derivative and volatility attributes were calculated from the recorded gamma ray and density logs,providing sixteen log features for the ML models to select from.A novel,multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation.Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation.When the trained ML models were applied to a third well for testing,lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features.However,an accuracy of~0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well.A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with~0.6 accuracy.Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.
