A method for predicting the water-flowing fractured zone height based on an improved key stratum theory

In the process of using the original key stratum theory to predict the height of a water-flowing fractured zone(WFZ),the influence of rock strata outside the calculation range on the rock strata within the calculation range as well as the fact that the shape of the overburden deformation area will change with the excavation length are ignored.In this paper,an improved key stratum theory(IKS theory)was proposed by fixing these two shortcomings.Then,a WFZ height prediction method based on IKS theory was established and applied.First,the range of overburden involved in the analysis was determined according to the tensile stress distribution range above the goaf.Second,the key stratum in the overburden involved in the analysis was identified through IKS theory.Finally,the tendency of the WFZ to develop upward was determined by judging whether or not the identified key stratum will break.The proposed method was applied and verified in a mining case study,and the reasons for the differences in the development patterns between the WFZs in coalfields in Northwest and East China were also fully explained by this method.

Genomic selection of eight fruit traits in pear

Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study,we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cost. We evaluated GS under two scenarios:(1) five-fold cross-validation in an interspecific pear family;(2) independent validation. Based on the cross-validation scheme, the prediction accuracy (PA) of eight fruit traits varied between 0.33 (fruit core vertical diameter)and 0.65 (stone cell content). Except for single fruit weight, a slightly better prediction accuracy (PA) was observed for the five parametrical methods compared with the two non-parametrical methods. In our TP of 310 individuals, 2 000 single nucleotide polymorphism (SNP) markers were sufficient to make reasonably accurate predictions. PAs for different traits increased by 18.21%-46.98%when the TP size increased from 50to 100, but the increment was smaller (-4.13%-33.91%) when the TP size increased from 200 to 250. For independent validation, the PAs ranged from 0.11 to 0.45 using rrBLUP method. In summary, our results showed that the TP size and SNP numbers had a greater impact on the PA than prediction methods. Furthermore, relatedness among the training and validation sets, and the complexity of traits should be considered when designing a TP to predict the test panel.

Interval finite difference method for steady-state temperature field prediction with interval parameters

A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.

A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

Application of Geophysical and Remote Sensing Methods to Predict for Potash Resource

1 Introduction Potassium is listed as one of the shortage of mineral resources in china.Geophysical and remote sensing technology plays an important role in prospecting for potash ressources.

AN EXTENSION OF THE METHOD FOR PREDICTING PERMEABILITY THROUGH POLYMER MEMBRANES FROM SIMPLE GASES TO WATER VAPOR

It is found that there is a linear relationship between log P-w, and the parameter term V-f/0.5 E(coh) [1+(delta(w) - delta(p))(2)/delta(p)(2), from the water permeability (P-w) data of 21 polymers covering 4 orders of magnitude. This correlation may be useful in choosing membrane materials for dehumidification of gases.

Method of Predicting Water Content in Crude Oil Based on Measuring Range Automatic Switching

Water content in output crude oil is hard to measure precisely because of wide range of dielectric coefficient of crude oil caused by injected dehydrating and demulsifying agents.The method to reduce measurement error of water content in crude oil proposed in this paper is based on switching measuring ranges of on-line water content analyzer automatically.Measuring precision on data collected from oil field and analyzed by in-field operators can be impressively improved by using back propogation （BP） neural network to predict water content in output crude oil.Application results show that the difficulty in accurately measuring water-oil content ratio can be solved effectively through this combination of on-line measuring range automatic switching and real time prediction,as this method has been tested repeatedly on-site in oil fields with satisfactory prediction results.

A NEW METHOD FOR INCREASING PRECISION IN SURVEY SAMPLING

This paper proposes a new method for increasing the precision in survey sam- pling, i.e., a method combining sampling with prediction. The two cases where auxiliary information is or not available are considered. A numerical example is given.

Comparison of TEC prediction methods in mid-latitudes with GIM maps

There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-latitude and equatorial regions. This paper compares the TEC prediction methods in the midlatitude zone according to the data of the Juliusruh, Rostov, Manzhouli stations in 2008 and 2015. For a long-term prediction, the IRI-Plas and Ne Quick models are compared with the Global Ionospheric Maps(GIM) presented by the Jet Propulsion Laboratory(JPL) and the Technical University of Catalonia(UPC).For a short-term prediction, the Standard Persistence Model(SPM) method, a 27 day median model, and the proposed short-term prediction method are compared for one day ahead. It is shown that for all stations the IRI-Plas model provides better compliance with GIM maps than the Ne Quick model irrespective of a solar activity level. An average absolute error lays in the range of 3 e3.5 TECU, relative root square mean(RMS) error in the range of 22 e27% in 2015 and 1.7 e2 TECU, 20 e25% in 2008. For the Ne Quick model, these estimates were 6.7 e8.2 TECU and 42 e45% in 2015 and 2.2 e3.6 TECU, 30 e37% in2008. For the short-term forecast, the best results were obtained by the SPM method with an average absolute error in the range of 1.95 e2.15 TECU in 2015 and 0.59 e0.98 TECU in 2008, a relative RMS error in the range of 17 e21% in 2015, 11.5 e15% in 2008. For the proposed short-term prediction method, these errors were 2.04 e2.2 TECU and 12 e14% in 2015 and 0.7 e1.0 TECU, 7 e11% in 2008. Using medians, the errors were 3.1 e3.4 TECU and 17 e21% in 2015 and 1.0 e1.3 TECU, 10 e15% in 2008. The dependence of results on the Dst-index was obtained.

Subsystem model-based close-loop grey-box identification method for hydraulic stewart platform

In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification method was put forward when consider the main coupling effects of hydraulic Stewart platform.Firstly,the whole system is divided into three TITO(Two Input Two Output) subsystems according to the characteristics of the pseudo-mass matrix,hence transfer function matrix model of the subsystem can also be found.Secondly,since the Stewart platform is unstable,the close-loop transfer model of the subsystem is derived under the proportional controllers.The inverse M serial is adopted as the identification signal to get the experimental data.All parameters of the subsystem are determined in close-loop indirect identification by PEM(Prediction Error Method).Finally,a case study validates the correctness and effectiveness of the subsystem model-based close-loop grey-box identification method for hydraulic Stewart platform.

The application of neural networks to comprehensive prediction by seismology prediction method

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

An improved oil recovery prediction method for volatile oil reservoirs

To describe the complex phase transformation in the process of depletion exploitation of volatile oil reservoir,four fluid phases are defined,and production and remaining volume of these phases are calculated based on the principle of surface volume balance,then the recovery prediction method of volatile oil reservoir considering the influence of condensate content in released solution gas and the correction method of multiple degassing experiments data are established.Taking three typical kinds of crude oil(black oil,medium-weak volatile oil,strong volatile oil)as examples,the new improved method is used to simulate constant volume depletion experiments based on the corrected data of multiple degassing experiment to verify the reliability of the modified method.By using"experimental data and traditional method","corrected data and traditional method"and"corrected data and modified method",recovery factors of these three typical kinds of oil are calculated respectively.The source of parameters and the calculation methods have little effect on the recovery of typical black oil.However,with the increase of crude oil volatility,the oil recovery will be seriously underestimated by using experimental data or traditional method.The combination of"corrected data and modified method"considers the influence of condensate in gas phase in both experimental parameters and calculation method,and has good applicability to typical black oil and volatile oil.The strong shrinkage of volatile oil makes more"liquid oil"convert to"gaseous oil",so volatile oil reservoir can reach very high oil recovery by depletion drive.

Model predictive inverse method for recovering boundary conditions of two-dimensional ablation

A model predictive inverse method (MPIM) is presented to estimate the time- and space-dependent heat flux onthe ablated boundary and the ablation velocity of the two-dimensional ablation system. For the method, first of all, therelationship between the heat flux and the temperatures of the measurement points inside the ablation material is establishedby the predictive model based on an influence relationship matrix. Meanwhile, the estimation task is formulated as aninverse heat transfer problem (IHTP) with consideration of ablation, which is described by an objective function of thetemperatures at the measurement point. Then, the rolling optimization is used to solve the IHTP to online estimate theunknown heat flux on the ablated boundary. Furthermore, the movement law of the ablated boundary is reconstructedaccording to the estimation of the boundary heat flux. The effects of the temperature measurement errors, the numberof future time steps, and the arrangement of the measurement points on the estimation results are analyzed in numericalexperiments. On the basis of the numerical results, the effectiveness of the presented method is clarified.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

PREDICTION OF FATIGUE LIVES OF RC BEAMS STRENGTHENED WITH CFL UNDER RANDOM LOADING

The investigation on fatigue lives of reinforced concrete （RC） structures strength- ened with fiber laminate under random loading is important for the repairing or the strengthening of bridges and the safety of the traffic. In this paper, two methods are developed for predicting the fatigue lives of RC structures strengthened with carbon fiber [aminate （CFL） under random loading based on a residual life and a residual strength model. To discuss the efficiency of the model, 12 RC beams strengthened with CFL are tested under random loading by the MTS810 testing system. The predicted residual strength approximately agrees with test results.

高压直流换流站中滤波电容器的振动与可听噪声(英文)

The filter capacitor stack is one of the main acoustic noise sources in high-voltage DC(HVDC) converter stations.As HVDC systems are built more and more recently,it is significant to research the audible noise of filter capacitors.In this paper,the current situation of research on vibration and audible noise of filter capacitors in HVDC converter stations,which is departed into three parts—generation mechanism,prediction methods,and reduction measures,is presented and the research achievements are discussed.Scholars have built the model that the alternating electric force caused by the voltage conduces to the vibration,which propagates to the enclosure and radiates audible noise.As a result,the parts contributing most to the generation of audible noise are the top and the bottom of capacitors. In the noise level prediction respect,several methods have been prospected including impact hammer,sweep frequency, impact current,monopole and Kirchhoff formula method,which are suitable for single capacitors or capacitors stacks individually.However,the sweep frequency method is restricted by experiment condition,and the impact current method needs further research and verified.On the other hand,CIGRE WG14.26 provides three sound reduction measures,but all of them are not so practicable,while MPP absorber and compressible space absorber prospected by Dr.Wu Peng are proved to be effective.The sound barriers are also considered by scholars,and the acoustic directivity performance of capacitors is also researched.Besides,the developing direction of each research field is prospected in corresponding part.

Statistical analysis on energy field of seismicity in Ningxia and its neighborhood region

The random function theory is used in the paper. Taking the regional seismicity energy as the random function of space and time, the features of small seismicity field in Ningxia and its neighborhood region are studied by the analytical method of natural orthogonal function expansion. The chief part of the field, i.e., the temporal changes of time weight coefficients of first several typical fields is analyzed. We have found that their values had upward and downward changes of a large amplitude before moderate-strong earthquakes and showed variation features correlating to moderate-strong earthquakes occurred in the region and its surrounding areas. From the earthquake examples in Ningxia region, we can make the conclusion that the method of natural orthogonal function expansion of earthquake energy stochastic field is an earthquake analysis and prediction method that is worth further exploration.

Prediction of DC Corona Onset Voltage for Rod-Plane Air Gaps by a Support Vector Machine

This paper proposes a new method to predict the corona onset voltage for a rod- plane air gap, based on the support vector machine （SVM）. Because the SVM is not limited by the size, dimension and nonlinearity of the samples, this method can realize accurate prediction with few training data. Only electric field features are chosen as the input; no geometric parameter is included. Therefore, the experiment data of one kind of electrode can be used to predict the corona onset voltages of other electrodes with different sizes. With the experimental data obtained by ozone detection technology, and experimental data provided by the reference, the efficiency of the proposed method is validated. Accurate predicted results with an average relative less than 3% are obtained with only 6 experimental data.

DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants

Genomic prediction is an effective way to accelerate the rate of agronomic trait improvement in plants.Traditional methods typically use linear regression models with clear assumptions;such methods are unable to capture the complex relationships between genotypes and phenotypes.Non-linear models(e.g.,deep neural networks)have been proposed as a superior alternative to linear models because they can capture complex non-additive effects.Here we introduce a deep learning(DL)method,deep neural network genomic prediction(DNNGP),for integration of multi-omics data in plants.We trained DNNGP on four datasets and compared its performance with methods built with five classic models:genomic best linear unbiased prediction(GBLUP);two methods based on a machine learning(ML)framework,light gradient boosting machine(LightGBM)and support vector regression(SVR);and two methods based on a DL framework,deep learning genomic selection(DeepGS)and deep learning genome-wide association study(DLGWAS).DNNGP is novel in five ways.First,it can be applied to a variety of omics data to predict phenotypes.Second,the multilayered hierarchical structure of DNNGP dynamically learns features from raw data,avoiding overfitting and improving the convergence rate using a batch normalization layer and early stopping and rectified linear activation(rectified linear unit)functions.Third,when small datasets were used,DNNGP produced results that are competitive with results from the other five methods,showing greater prediction accuracy than the other methods when large-scale breeding data were used.Fourth,the computation time required by DNNGP was comparable with that of commonly used methods,up to 10 times faster than DeepGS.Fifth,hyperparameters can easily be batch tuned on a local machine.Compared with GBLUP,LightGBM,SVR,DeepGS and DLGWAS,DNNGP is superior to these existing widely used genomic selection(GS)methods.Moreover,DNNGP can generate robust assessments from diverse datasets,including omics data,and quickly incorporate complex and large datasets into usable models,making it a promising and practical approach for straightforward integration into existing GS platforms.