用ABCD^2评分法预测短暂性脑缺血发作短期进展为脑梗死的风险

目的探讨ABCD2评分对短暂性脑缺血发作后7天内发生脑梗死的预测价值。方法按照ABCD评分标准和ABCD2评分标准,测定179例TIA患者的评分并观察7天内脑梗死的发生率。结果AB-CD2评分法和ABCD评分法的曲线下面积分别为0.761、0.699。179例TIA患者中≤2分者29例,脑梗死的发生率为零;评分为3分者22例,脑梗死的发生率为9%;评分为4分者58例,脑梗死的发生率为24%;评分为5分者53例,脑梗死的发生率为42%;评分≥6分者17例,脑梗死的发生率为65%。低危(0~3分)、中危(4~5分)和高危(6~7分)组7天内发生脑梗死的比例分别为4%、32%和65%(P<0.05)。结论ABCD2评分法的预测价值高于ABCD评分法,是临床上预测TIA短期进展为脑梗死的一种有效方法。

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Comparison of the City Water Consumption Short-Term Forecasting Methods

There are a lot of methods in city water consumption short-term forecasting both inside and outside the country. But among these methods there exist many advantages and shortcomings in model establishing, solving and predicting accuracy, speed, applicability. This article draws lessons from other realm mature methods after many years′ study. It′s systematically studied and compared to predict the water consumption in accuracy, speed, effect and applicability among the time series triangle function method, artificial neural network method, gray system theories method, wavelet analytical method.

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

SVM method for predicting the thickness of sandstone

The Support Vector Machine （SVM） method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method.

Prediction of the Hualian Earthquakes in Taiwan and an Extended Discussion on the Method of Commensurability

The method of commensurability was used by the authors to predict the great earthquake of magnitude 7.5 that occurred on March 31,2002 in Taiwan 70km away from Hualian. Analyzing the earthquakes of magnitude≥7.0 which occurred in the Hualian area of Taiwan within the 20th century, the authors discovered that the occurrences of the earthquakes are commensurable. The earthquakes of magnitude 7.6 which occurred in Hualian of Taiwan, on September 20th, 1999 and of magnitude 7.5 which occurred 70 km away from Hualian, on March 31th, 2002 appeared at the commensurable point of K=2 and the period times the golden section, respectively. An extended discussion is carried out on the method of commensurability and its implied physical significance, especially on the contribution of the commensurable periodic extension made by Prof. Weng Wenbo.

Prediction of Injection-Production Ratio with BP Neural Network

Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.

The constructing of pore structure factor in carbonate rocks and the inversion of reservoir parameters

With a more complex pore structure system compared with clastic rocks, carbonate rocks have not yet been well described by existing conventional rock physical models concerning the pore structure vagary as well as the influence on elastic rock properties. We start with a discussion and an analysis about carbonate rock pore structure utilizing rock slices. Then, given appropriate assumptions, we introduce a new approach to modeling carbonate rocks and construct a pore structure algorithm to identify pore structure mutation with a basis on the Gassmann equation and the Eshelby-Walsh ellipsoid inclusion crack theory. Finally, we compute a single well＇s porosity using this new approach with full wave log data and make a comparison with the predicted result of traditional method and simultaneously invert for reservoir parameters. The study results reveal that the rock pore structure can significantly influence the rocks＇ elastic properties and the predicted porosity error of the new modeling approach is merely 0.74%. Therefore, the approach we introduce can effectively decrease the predicted error of reservoir parameters.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

Prediction for asphalt pavement water film thickness based on artificial neural network

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.

Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques

Rockburst prediction is of vital significance to the design and construction of underground hard rock mines.A rockburst database consisting of 102 case histories,i.e.,1998−2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods.The dataset was examined with six widely accepted indices which are:the maximum tangential stress around the excavation boundary(MTS),uniaxial compressive strength(UCS)and uniaxial tensile strength(UTS)of the intact rock,stress concentration factor(SCF),rock brittleness index(BI),and strain energy storage index(EEI).Two boosting(AdaBoost.M1,SAMME)and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated.The available dataset was randomly divided into training set(2/3 of whole datasets)and testing set(the remaining datasets).Repeated 10-fold cross validation(CV)was applied as the validation method for tuning the hyper-parameters.The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles.According to 10-fold CV,the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1,SAMME algorithms and empirical criteria methods.

Prediction of Gas Holdup in Bubble Columns Using Artificial Neural Network

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%.

Prediction Technology of Buried Water-Bearing Structures in Coal Mines Using Transient Electromagnetic Method

Buried water-conducting and water-bearing structures in front of the driving head may easily lead to water bursts in coal mines. Therefore,it is very important for the safety of production to make an accurate and timely forecast about water bursts. Based on the smoke ring effect of transient electromagnetic fields,the principle of transient electro-magnetic method used in detecting buried water-bearing structures in coal mines in advance,is discussed. Small multi-turn loop configurations used in coal mines are proposed and a field procedure of semicircular sector scanning is presented. The application of this method in one coal mine indicates that the technology has many advantages compared with others. The method is inexpensive,highly accurate and efficient. Suggestions are presented for future solutions to some remaining problems.

A Selective Moving Window Partial Least Squares Method and Its Application in Process Modeling

A selective moving window partial least squares(SMW-PLS) soft sensor was proposed in this paper and applied to a hydro-isomerization process for on-line estimation of para-xylene(PX) content. Aiming at the high frequency of model updating in previous recursive PLS methods, a selective updating strategy was developed. The model adaptation is activated once the prediction error is larger than a preset threshold, or the model is kept unchanged.As a result, the frequency of model updating is reduced greatly, while the change of prediction accuracy is minor.The performance of the proposed model is better as compared with that of other PLS-based model. The compromise between prediction accuracy and real-time performance can be obtained by regulating the threshold. The guidelines to determine the model parameters are illustrated. In summary, the proposed SMW-PLS method can deal with the slow time-varying processes effectively.

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

Failure analysis study of railway draw-hook coupler

Failure analysis of railway draw-hook coupler was carried out.The nondestructive testing method was undertaken on some failed couplers in service to designate critical areas of a coupler.Draw-Hook coupler is used to connect with the same hook coupler or automatic coupler.The influence of each connection types on the coupler strength in this study was discussed.A numerical stress analysis using FEM was performed,and many approaches including critical plane approach were carried out on fatigue life prediction of coupler under different conditions.The results of the proposed fatigue criterion and fatigue life predictions,as well as static numerical analysis,are validated with experimental results.

Pattern recognition prediction of coal and gas outburst hazard in the sixth mine of Hebi

Based on the systematical analysis influence factors of coal and gas outburst, the main factors and their magnitude was determined by the corresponding methods.With the research region divided into finite predicting units,the internal relation between the factors and the hazard of coal and gas outburst,that was combination model of influence factors,was ascertained through multi-factor pattern recognition method.On the basis of contrastive analysis the pattern of coal and gas outburst between prediction region and mined region,the hazard of every predication unit was determined.The mining area was then divided into coal and gas outburst dangerous area,threaten area and safe area re- spectively according to the hazard of every predication unit.Accordingly the hazard of mining area is assessed.

The Curl of Q Vector: A New Diagnostic Parameter Associated with Heavy Rainfall

As a powerful tool to diagnose vertical motion, frontogenesis, and secondary circulation, the Q vector and its divergence are widely used. However, little attention has been given to the curl of Q vector. In this paper, a new set of analyses combining the divergence of the Q vector （DQ） with the vertical component of the curl of the Q vector （VQ） is applied to a Northeastern cold vortex rainfall case. From the derivation, it was found that the expressions of the Q vectors and their divergences in saturated moist flow （DQm） differ from those of dry and unsaturated moist atmosphere （DQ）, while the VQs of various background flows are exactly the same, which largely simplified the analyses. This case study showed that, compared with the DQ, not only can the DQm diagnose precipitation more effectively, but the VQ may also be indicative of precipitation （especially for heavy rainfall and strong convection） because of its direct, close relationship with ageostrophic motion. Thus, the VQ may be computed and analyzed with ease, and may serve as a useful tool for analyses of precipitation and strong convective svstems.

Spectral decomposition method for predicting magmatic intrusion into a coal bed

Accurate prediction of magmatic intrusion into a coal bed is illustrated using the method of seismic spectral decomposition.The characteristics of coal seismic reflections are first analyzed and the effect of variable time windows and domain frequencies on the spectral decomposition are examined.The higher domain frequency of coal bed reflections using the narrower STFT time window,or the smaller ST scale factor,are acceptable.When magmatic rock intrudes from the bottom of the coal bed the domain frequency of the reflections is decreased slightly,the frequency bandwidth is narrowed correspondingly,and the response from spectral decomposition is significantly reduced.Intrusion by a very thin magmatic rock gives a spectral decomposition response that is just slightly less than what is seen from a normal coal bed.Results from an actual mining area were used to validate the method.Predicting the boundary of magmatic intrusions with the method discussed herein was highly accurate and has been validated by observations from underground mining.

Seismic attributes optimization and application in reservoir prediction

Petroleum geophysicists recognize that many parameters related to oil and gas reservoirs are predicted using seismic attribute data. However, how best to optimize the seismic attributes, predict the character of thin sandstone reservoirs, and enhance the reservoir description accuracy is an important goal for geologists and geophysicists. Based on the theory of main component analysis, we present a new optimization method, called constrained main component analysis. Modeling estimates and real application in an oilfield show that it can enhance reservoir prediction accuracy and has better applicability.