Surface subsidence due to underground mining operation under weak geological condition in Indonesia

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.

A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

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.

Extending OpenStack Monasca for Predictive Elasticity Control

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.

A hybrid deep neural network based prediction of 300 MW coalfired boiler combustion operation condition

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.