1.公开(公告)号:US20230323760A1。摘要:Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drlling operat...1.公开(公告)号:US20230323760A1。摘要:Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drlling operations at a subsurface region.展开更多
The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing ...The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing datasets for the emissions of three utility boilers, a 3-layer back-propagation network is applied to predict the mercury speciation at the stack. The whole prediction procedure includes： collection of data, structuring an artificial neural network （ANN） model, training process and error evaluation. A total of 59 parameters of coal and ash analyses and power plant operating conditions are treated as input variables, and the actual mercury emissions and their speciation data are used to supervise the training process and verify the performance of prediction modeling. The precision of model prediction （ root- mean-square error is 0. 8 μg/Nm3 for elemental mercury and 0. 9 μg/Nm3 for total mercury） is acceptable since the spikes of semi- mercury continuous emission monitor （SCEM） with wet conversion modules are taken into consideration.展开更多
H. Schuh, M. Ulrich, D. Egger, J. Mueller, W. Schwegmann: Prediction of Earth orientation parameters by artificial neural networks: 247-258 利用人工神经网络预测地球定向参数 K. -R. Koch, J. Kusche: Regularization of ge...H. Schuh, M. Ulrich, D. Egger, J. Mueller, W. Schwegmann: Prediction of Earth orientation parameters by artificial neural networks: 247-258 利用人工神经网络预测地球定向参数 K. -R. Koch, J. Kusche: Regularization of geopotential determination from satellite data by variance components: 259-268展开更多
The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-...The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.展开更多
With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantita...With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.展开更多
A new correlation for the prediction of gas hold up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years. The updated estimation method rel...A new correlation for the prediction of gas hold up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years. The updated estimation method relying on artificial neural network, dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.展开更多
The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boul...The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.展开更多
In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural netw...In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.展开更多
Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of co...Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.展开更多
Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutua...Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.展开更多
文摘1.公开(公告)号:US20230323760A1。摘要:Methods and systems,including computer programs encoded on a computer storage medium are described for implementing a system that predicts wireline logs used in well drlling operations at a subsurface region.
基金The National Basic Research Program of China (973Program) (No.2006CB200302)the Natural Science Foundation of JiangsuProvince (No.BK2007224).
文摘The feasibility of using an ANN method to predict the mercury emission and speciation in the flue gas of a power station under un-tested combustion/operational conditions is evaluated. Based on existing field testing datasets for the emissions of three utility boilers, a 3-layer back-propagation network is applied to predict the mercury speciation at the stack. The whole prediction procedure includes： collection of data, structuring an artificial neural network （ANN） model, training process and error evaluation. A total of 59 parameters of coal and ash analyses and power plant operating conditions are treated as input variables, and the actual mercury emissions and their speciation data are used to supervise the training process and verify the performance of prediction modeling. The precision of model prediction （ root- mean-square error is 0. 8 μg/Nm3 for elemental mercury and 0. 9 μg/Nm3 for total mercury） is acceptable since the spikes of semi- mercury continuous emission monitor （SCEM） with wet conversion modules are taken into consideration.
文摘H. Schuh, M. Ulrich, D. Egger, J. Mueller, W. Schwegmann: Prediction of Earth orientation parameters by artificial neural networks: 247-258 利用人工神经网络预测地球定向参数 K. -R. Koch, J. Kusche: Regularization of geopotential determination from satellite data by variance components: 259-268
文摘The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.
文摘With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.
基金Supported by the National Natural Science Foundation of China(No.20076036)and Education Department of Hubei Province.
文摘A new correlation for the prediction of gas hold up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years. The updated estimation method relying on artificial neural network, dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.
文摘The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.
基金The National Natural Science Foundation of China(No.51478114,51778136)the Transportation Science and Technology Program of Liaoning Province(No.201532)
文摘In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.
基金Projects 50874103 supported by the National Natural Science Foundation of China2006CB202210 by the National Basic Research Program of China+1 种基金BK2008135 by the Natural Science Foundation of Jiangsu Provincethe Qing-lan Project of Jiangsu Province
文摘Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.
文摘Based on the comparison of several methods of time series predicting, this paper points out that it is necessary to use dynamic neural network in modeling of complex production process. Because self feedback and mutual feedback are adopted among nodes at the same layer in Elman network, it has stronger ability of dynamic approximation, and can describe any non linear dynamic system. After the structure and mathematical description being given, dynamic back propagation (BP) algorithm of training weights of Elman neural network is deduced. At last, the network is used to predict ash content of black amber in jigging production process. The results show that this neural network is powerful in predicting and suitable for modeling, predicting, and controling of complex production process.
