Forecasting convective storms using NWP models is an important goal and a highly active area of ongoing research. Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead...Forecasting convective storms using NWP models is an important goal and a highly active area of ongoing research. Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead times, as opera- tional forecasters begin to incorporate convective-scale fore- casts into severe weather forecast operations （Stensrud et al., 2009, 2013）. This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards.展开更多
Subsidence analysis and prediction with measured data have been conducted and applied to local strata and mining conditions worldwide. Underground coal mines chose the most suitable analysis and prediction method for ...Subsidence analysis and prediction with measured data have been conducted and applied to local strata and mining conditions worldwide. Underground coal mines chose the most suitable analysis and prediction method for them. However, there was no study based on the measured data of subsidence induced by underground mining operation in Indonesia. This paper describes the condition of underground coal mine in Indonesia and then discusses the subsidence behavior due to longwall mining operation based on measured data in Balikpapan coal-bearing formation in Indonesia.展开更多
Objective: to study the clinical value of predictive nursing model in patients with hepatolithiasis undergoing operation. Methods: 68 patients who needed hepatolithiasis surgery in our hospital from January 2019 to Ja...Objective: to study the clinical value of predictive nursing model in patients with hepatolithiasis undergoing operation. Methods: 68 patients who needed hepatolithiasis surgery in our hospital from January 2019 to January 2020 were selected as the research object, and they were randomly divided into the research group and the reference group, with 34 patients in each group. They were given predictive nursing and routine nursing respectively, and then the nursing situation of the patients was fully analyzed. Results: through the comparative analysis of hospital stay and bed rest time after nursing, it was found that the time of the reference group was significantly shorter than that of the research group leader (P<0.05). Moreover, the incidence of complications and the residual rate of hepatolithiasis in the study group were significantly lower than those in the reference group. Conclusion: in the nursing of hepatolithiasis, the predictive nursing method has very significant significance in the prognosis of patients' therapeutic effect, and has a great promotion effect on patients' physical recovery.展开更多
In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operatio...In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.展开更多
Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal c...Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.展开更多
Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption metrics.Managing such rules at scale is cumbersome,especial...Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption metrics.Managing such rules at scale is cumbersome,especially when resources require non-negligible time to be instantiated.This paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system.In this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when needed.The proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics.We use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional policy.To assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)workload.In particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting overhead.The implementation of our architecture is open-source.展开更多
文摘Forecasting convective storms using NWP models is an important goal and a highly active area of ongoing research. Skillful and reliable NWP of convective storms could allow for severe weather warnings with longer lead times, as opera- tional forecasters begin to incorporate convective-scale fore- casts into severe weather forecast operations （Stensrud et al., 2009, 2013）. This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards.
文摘Subsidence analysis and prediction with measured data have been conducted and applied to local strata and mining conditions worldwide. Underground coal mines chose the most suitable analysis and prediction method for them. However, there was no study based on the measured data of subsidence induced by underground mining operation in Indonesia. This paper describes the condition of underground coal mine in Indonesia and then discusses the subsidence behavior due to longwall mining operation based on measured data in Balikpapan coal-bearing formation in Indonesia.
文摘Objective: to study the clinical value of predictive nursing model in patients with hepatolithiasis undergoing operation. Methods: 68 patients who needed hepatolithiasis surgery in our hospital from January 2019 to January 2020 were selected as the research object, and they were randomly divided into the research group and the reference group, with 34 patients in each group. They were given predictive nursing and routine nursing respectively, and then the nursing situation of the patients was fully analyzed. Results: through the comparative analysis of hospital stay and bed rest time after nursing, it was found that the time of the reference group was significantly shorter than that of the research group leader (P<0.05). Moreover, the incidence of complications and the residual rate of hepatolithiasis in the study group were significantly lower than those in the reference group. Conclusion: in the nursing of hepatolithiasis, the predictive nursing method has very significant significance in the prognosis of patients' therapeutic effect, and has a great promotion effect on patients' physical recovery.
基金supported by the National Natural Science Foundation of China(Grant No.51976038)the Natural Science Foundation of Jiangsu Province,China for Young Scholars(Grant No.BK20190366)the China Scholarship Council(Grant No.202006090164).
文摘In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.
基金supported by the National Natural Science Foundation of China(Grant 52207180)Xi'an High Voltage Apparatus Research Institute Co.Ltd.(Grant K222301-01)the Anhui Provincial Natural Science Foundation(Grant 2208085UD18).
文摘Quickly and accurately obtaining the internal temperature distribution of a transformer plays a key role in predicting its operating conditions and simplifying the maintenance process.A reasonable equivalent thermal circuit model is a relatively reliable method of obtaining the internal temperature distribution.However,thermal circuit models without targeted consideration of operating conditions and parameter corrections usually limit the accuracy of the results.This paper proposed a five-node transient thermal circuit model with the introduction of nonlinear thermal resistance,which considered the internal structure and winding layout of the core-type high-frequency transformer.The Nusselt number,a crucial variable in heat convection calculations and directly related to the accuracy of thermal resistance parameters,was calibrated on the basis of the distribution of external cooling air.After parameter calibration,the maximum computational error of the hotspot temperature is reduced by 5.48%compared with that of the uncalibrated model.Finally,an experimental platform for temperature monitoring was established to validate the five-node model and its ability to track the temperature change at each reference point after calibrating the Nusselt number.
基金supported by the PNRR-M4C2-Investimento 1.3,Partenariato Esteso(No.PE00000013-FAIR).
文摘Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption metrics.Managing such rules at scale is cumbersome,especially when resources require non-negligible time to be instantiated.This paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system.In this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when needed.The proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics.We use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional policy.To assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)workload.In particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting overhead.The implementation of our architecture is open-source.
