Forming Condition and Geology Prediction Techniques of Deep Clastic Reservoirs

1 Introduction As new exploration domain for oil and gas,reservoirs with low porosity and low permeability have become a hotspot in recent years(Li Daopin,1997).With the improvement of technology,low porosity and low

Prediction of Aircraft Engine Health Condition Parameters Based on Ensemble ELM

In view of aircraft engine health condition parameters prediction,an ensemble ELM based prediction approach is proposed in this paper. In the approach,the AdaBoost. RT algorithm is improved to adjust its threshold adaptively,and is utilized as the basic framework to establish the ensemble learning model using ELM as weak learners. The proposed approach is evaluated through the prediction of the actual engine fuel flow deviation time series,and the results demonstrate that this approach is feasible for the prediction of aircraft engine health condition parameters. The performance of the proposed approach is compared with single ELM, single process neural network ( PNN) ,and a similar ensemble ELM based approach using AdaBoost. RT as basic framework. The results show that,the proposed approach is more accurate than single ELM and single PNN,and no worse than the ensemble prediction approach for contrast,furthermore,the given approach is more convenient for practical application. Therefore,the proposed approach is better suited to the prediction of aircraft engine health parameters.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

Prediction of Sewer Pipe Main Condition Using the Linear Regression Approach

This research presents the condition prediction of sewer pipes using a linear regression approach. The analysis is based on data obtained via Closed Circuit Television (CCTV) inspection over a sewer system. Information such as pipe material and pipe age is collected. The regression approach is developed to evaluate factors which are important and predict the condition using available information. The analysis reveals that the method can be successfully used to predict pipe condition. The specific model obtained can be used to assess the pipes for the given sewer system. For other sewer systems, the method can be directly applied to predict the condition. The results from this research are able to assist municipalities to forecast the condition of sewer pipe mains in an effort to schedule inspection, allocate budget and make decisions.

A Contrastive Analysis of Conditionals in English and Moroccan Arabic: Potential problems when learning English conditionals by Moroccan EFL learners

Learning to handle hypothetical situations in a new language is always difficult(Catford,et al.,1974).This rule holds true for Moroccan Arabic(henceforth MA)speakers learning English because grammatical devices in the two languages differ in almost all equivalent situations.For instance,while English verb forms are used to indicate tense in conditional sentences,MA uses them to indicate aspect.Adopting the typology of conditional constructions suggested by Dancygier(1999)and Dancygier&Sweetser(2005),this study provides a contrastive analysis of conditionals in English and MA to predict the possible errors EFL/ESL learners are likely to make while learning English.The analysis shows that the main discrepancy between English conditionals and MA conditionals lies in the verb form used by the two systems.Accordingly,if EFL/ESL learners are influenced by verb form in their L1,they are likely to face some challenges while learning English conditionals.That is,they are likely to use the past tense in the protases of English predictive conditionals and generic conditionals since the perfective form of the verb is used in the protases of these two types in MA.Concerning the protases of English non-predictive conditionals,Moroccan EFL/ESL learners are likely to use either the past tense or the present tense since both the perfective and the imperfective forms of the verb are possible in the protases of MA non-predictive conditionals.However,due to the fact that the perfective form is the prototypical form in the protases of conditionals in MA,EFL/ESL learners are likely to use the past tense more often than the present tense.The analysis also shows that EFL/ESL learners tend to use the present tense in the apodoses of English conditionals since the prevalent form in the apodoses of MA conditionals is the imperfective.

Trend Prediction Method Based on the Largest Lyapunov Exponent for Large Rotating Machine Equipments

In order to predict electromechanical equipments＇ nonlinear and non-stationary condition effectively, max Lyapunov exponent is introduced to the fault trend prediction of large rotating mechanical equipments based on chaos theory. The predict method of chaos time series and two methods of proposing f and F are dis- cussed. The arithmetic of max prediction time of chaos time series is provided. Aiming at the key part of large rotating mechanical equipments-bearing, used this prediction method the simulation experiment is carried out. The result shows that this method has excellent performance for condition trend prediction.

Impact of observational MJO forcing on ENSO predictability in the Zebiak-Cane model: PartⅠ.Effect on the maximum prediction error

With the observational wind data and the Zebiak-Cane model, the impact of Madden-Iulian Oscillation （MJO） as external forcing on El Nino-Southern Oscillation （ENSO） predictability is studied. The observational data are analyzed with Continuous Wavelet Transform （CWT） and then used to extract MJO signals, which are added into the model to get a new model. After the Conditional Nonlinear Optimal Perturbation （CNOP） method has been used, the initial errors which can evolve into maximum prediction error, model errors and their join errors are gained and then the Nifio 3 indices and spatial structures of three kinds of errors are investigated. The results mainly show that the observational MJO has little impact on the maximum prediction error of ENSO events and the initial error affects much greater than model error caused by MJO forcing. These demonstrate that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the adaptive data assimilation of the ENSO forecast and contribute to the ENSO target observation.

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.

Resource saving based dwell time allocation and detection threshold optimization in an asynchronous distributed phased array radar network

The resource optimization plays an important role in an asynchronous Phased Array Radar Network(PARN)tracking multiple targets with Measurement Origin Uncertainty(MOU),i.e.,considering the false alarms and missed detections.A Joint Dwell Time Allocation and Detection Threshold Optimization(JDTADTO)strategy is proposed for resource saving in this case.The Predicted Conditional Cramér-Rao Lower Bound(PC-CRLB)with Bayesian Detector and Amplitude Information(BD-AI)is derived and adopted as the tracking performance metric.The optimization model is formulated as minimizing the difference between the PC-CRLBs and the tracking precision thresholds under the constraints of upper and lower bounds of dwell time and false alarm ratio.It is shown that the objective function is nonconvex due to the Information Reduction Factor(IRF)brought by the MOU.A cyclic minimizer-based solution is proposed for problem solving.Simulation results confirm the flexibility and robustness of the JDTADTO strategy in both sufficient and insufficient resource scenarios.The results also reveal the effectiveness of the proposed strategy compared with the strategies adopting the BD without detection threshold optimization and amplitude information.

Model-averaging-based semiparametric modeling for conditional quantile prediction

In real data analysis,the underlying model is frequently unknown.Hence,the modeling strategy plays a key role in the success of data analysis.Inspired by the idea of model averaging,we propose a novel semiparametric modeling strategy for the conditional quantile prediction,without assuming that the underlying model is any specific parametric or semiparametric model.Due to the optimality of the weights selected by leaveone-out cross-validation,the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models.Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure.We conducted extensive simulations to compare our method with alternatives across various scenarios.The results show that our method provides more accurate predictions.Finally,we applied our approach to the Boston housing data,yielding more precise quantile predictions of house prices compared with commonly used methods,and thus offering a clearer picture of the Boston housing market.

Multiaxial fatigue life prediction of composite materials

In order to analyze the stress and strain fields in the fibers and the matrix in composite materials,a fiber-scale unit cell model is established and the corresponding periodical boundary conditions are introduced.Assuming matrix cracking as the failure mode of composite materials,an energy-based fatigue damage parameter and a multiaxial fatigue life prediction method are established.This method only needs the material properties of the fibers and the matrix to be known.After the relationship between the fatigue damage parameter and the fatigue life under any arbitrary test condition is established,the multiaxial fatigue life under any other load condition can be predicted.The proposed method has been verified using two different kinds of load forms.One is unidirectional laminates subjected to cyclic off-axis loading,and the other is filament wound composites subjected to cyclic tension-torsion loading.The fatigue lives predicted using the proposed model are in good agreements with the experimental results for both kinds of load forms.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.