[Objective] This study aimed to establish models based on atmospheric cir- culation indices for forecasting the area attacked by rice planthopper every year, and to provide guide for preventing and controlling plantho...[Objective] This study aimed to establish models based on atmospheric cir- culation indices for forecasting the area attacked by rice planthopper every year, and to provide guide for preventing and controlling planthopper damage. [Method] The data related to rice planthopper occurrence and atmospheric circulation were collected and analyzed with the method of stepwise regression to establish the prediction models. [Result] The factors significantly related to the area attacked by rice plan-thopper were selected. Two types of prediction models were established. One was for Sogatella furcifera （Horvath）, based on Atlantic-Europe circulation pattern W in October in that year, Pacific polar vortex area index in October in that year, North America subtropical high index in August in that year, Atlantic-Europe circulation pattern W in June in that year, northern boundary of North America subtropical high in February in that year, Atlantic-Europe polar vortex intensity index in October in that year and Asia polar vortex intensity index in November in the last year; the other type of prediction models were for Nilaparvata lugens （Stal）, based on the Eastern Pacific subtropical high intensity index in July in that year, northern hemi- sphere polar vortex area index in October in the last year, Asia polar vortex strength index in November in the last year, north boundary of North America-At- lantic subtropical high in September in that year, north boundary of North Africa-At- lantic-North America subtropical high in January in that year, sunspot in September of the last year and eastern Pacific subtropical high area index in September in that year. [Conclusion] With the stepwise regression, the forecasting equations of the rice planthopper occurrence established based on the atmospheric circulation indices could be used for actual forecast.展开更多
Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic...Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.展开更多
In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbo...In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbor pixels. An algorithm of ascertaining available QTM neighbors is also proposed. Then, the method for reducing space complexities in the procedure of predicting QTM pixel values is presented. Next, the structure for storing compressed QTM pixel is proposed. In the end, the experiment on comparing compression ratio of this method with other methods is carried out by using three wave bands data of 1 km resolution of NOAA images in China. The results indicate that: 1) the compression method performs better than any other, such as Run Length Coding, Arithmetic Coding, Huffman Cod- ing, etc; 2) the average size of compressed three wave band data based on the neighbor QTM pixel predictive model is 31.58% of the origin space requirements and 67.5% of Arithmetic Coding without predictive model.展开更多
Element parameters including volume filled ratio,surface dimensionless distance,and surface filled ratio for DFDM(direct finite difference method)were proposed to describe shape and location of free surfaces in castin...Element parameters including volume filled ratio,surface dimensionless distance,and surface filled ratio for DFDM(direct finite difference method)were proposed to describe shape and location of free surfaces in casting mold filling processes.A mathematical model of the filling process was proposed specially considering the mass,momentum and heat transfer in the vicinity of free surfaces.Furthermore,a method for gas entrapment was established by tracking flow of entrapped gas.The model and method were applied to practical ADC1 high pressure die castings.The gas entrapment prediction was compared with the fraction and maximum size of porosities in the different casting parts.The comparison shows validity of the proposed model and method.The study indicates that final porosities in high pressure die castings are dependent on both gas entrapment during mold filling process and pressure transfer within solidification period.展开更多
Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accu...Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.展开更多
Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing...Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.展开更多
Based on study of the influence of main roof fracture on ground pressure, this paper considered the immediate roof as a semi-infinite long beam on a Winkler elastic foundation. In the model the coal seam is the founda...Based on study of the influence of main roof fracture on ground pressure, this paper considered the immediate roof as a semi-infinite long beam on a Winkler elastic foundation. In the model the coal seam is the foundation and the pressure caused by mian roof deflection is the load. Having solved the model and analyzed relevant factors,the authors indicate that the disturbance caused by the breakage of the mian roof can be observed in both gates of longwall face and explain why it can be. The paper points out that the applicability of the method to obtain the disturbance information by measuring the loads on supports is wider than that by measuring the roof convergence rate. The results are useful for monitoring and predicting ground pressure.展开更多
Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square r...Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.展开更多
基金Supported by Special Fund for Agro-scientific Research in the Public Interest(200903051)~~
文摘[Objective] This study aimed to establish models based on atmospheric cir- culation indices for forecasting the area attacked by rice planthopper every year, and to provide guide for preventing and controlling planthopper damage. [Method] The data related to rice planthopper occurrence and atmospheric circulation were collected and analyzed with the method of stepwise regression to establish the prediction models. [Result] The factors significantly related to the area attacked by rice plan-thopper were selected. Two types of prediction models were established. One was for Sogatella furcifera （Horvath）, based on Atlantic-Europe circulation pattern W in October in that year, Pacific polar vortex area index in October in that year, North America subtropical high index in August in that year, Atlantic-Europe circulation pattern W in June in that year, northern boundary of North America subtropical high in February in that year, Atlantic-Europe polar vortex intensity index in October in that year and Asia polar vortex intensity index in November in the last year; the other type of prediction models were for Nilaparvata lugens （Stal）, based on the Eastern Pacific subtropical high intensity index in July in that year, northern hemi- sphere polar vortex area index in October in the last year, Asia polar vortex strength index in November in the last year, north boundary of North America-At- lantic subtropical high in September in that year, north boundary of North Africa-At- lantic-North America subtropical high in January in that year, sunspot in September of the last year and eastern Pacific subtropical high area index in September in that year. [Conclusion] With the stepwise regression, the forecasting equations of the rice planthopper occurrence established based on the atmospheric circulation indices could be used for actual forecast.
基金Project(2013CB036004)supported by the National Basic Research Program of ChinaProject(51378510)supported by the National Natural Science Foundation of China
文摘Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.
基金Project 40471108 supported by the National Natural Science Foundation of China
文摘In this paper, a new predictive model, adapted to QTM (Quaternary Triangular Mesh) pixel compression, is introduced. Our approach starts with the principles of proposed predictive models based on available QTM neighbor pixels. An algorithm of ascertaining available QTM neighbors is also proposed. Then, the method for reducing space complexities in the procedure of predicting QTM pixel values is presented. Next, the structure for storing compressed QTM pixel is proposed. In the end, the experiment on comparing compression ratio of this method with other methods is carried out by using three wave bands data of 1 km resolution of NOAA images in China. The results indicate that: 1) the compression method performs better than any other, such as Run Length Coding, Arithmetic Coding, Huffman Cod- ing, etc; 2) the average size of compressed three wave band data based on the neighbor QTM pixel predictive model is 31.58% of the origin space requirements and 67.5% of Arithmetic Coding without predictive model.
基金Project(50975093)supported by the National Natural Science Foundation of ChinaProject(08-0209)supported by New Century Excellent Talent in University,Ministry of Education,ChinaProject(2009ZM0283)supported by the Fundamental Research Funds for the Central Universities,China
文摘Element parameters including volume filled ratio,surface dimensionless distance,and surface filled ratio for DFDM(direct finite difference method)were proposed to describe shape and location of free surfaces in casting mold filling processes.A mathematical model of the filling process was proposed specially considering the mass,momentum and heat transfer in the vicinity of free surfaces.Furthermore,a method for gas entrapment was established by tracking flow of entrapped gas.The model and method were applied to practical ADC1 high pressure die castings.The gas entrapment prediction was compared with the fraction and maximum size of porosities in the different casting parts.The comparison shows validity of the proposed model and method.The study indicates that final porosities in high pressure die castings are dependent on both gas entrapment during mold filling process and pressure transfer within solidification period.
基金Project(2010CB732004)supported by the National Basic Research Program of ChinaProjects(50934006,41272304)supported by the National Natural Science Foundation of China
文摘Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.
文摘Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.
文摘Based on study of the influence of main roof fracture on ground pressure, this paper considered the immediate roof as a semi-infinite long beam on a Winkler elastic foundation. In the model the coal seam is the foundation and the pressure caused by mian roof deflection is the load. Having solved the model and analyzed relevant factors,the authors indicate that the disturbance caused by the breakage of the mian roof can be observed in both gates of longwall face and explain why it can be. The paper points out that the applicability of the method to obtain the disturbance information by measuring the loads on supports is wider than that by measuring the roof convergence rate. The results are useful for monitoring and predicting ground pressure.
基金Supported by the National Natural Science Foundation of China(50874005)Anhui Province College Young Teachers Scientific Research"Allotment Planning"Key Project(2009SQRZ067)
文摘Through analyzing experimental data of gas explosions in excavation roadwaysand the forecast models of the literature, Found that there is no direct proportional linearcorrelation between overpressure and the square root of the accumulated volume of gas,the square root of the propagation distance multiplicative inverse.Also, attenuation speedof the forecast model calculation is faster than that of experimental data.Based on theoriginal forecast models and experimental data, deduced the relation of factors by introducinga correlation coefficient with concrete volume and distance, which had been verifiedby the roadway experiment data.The results show that it is closer to the roadway experimentaldata and the overpressure amount increases first then decreases with thepropagation distance.
