Integrated Reservoir Prediction and Oil-Gas Evaluation in the Maoshan Area

The Maoshan area is an area with well-developed igneous rocks and complex structures. The thickness of the reservoirs is generally small. The study of the reservoirs is based on seismic data, logging data and geological data. Using techniques and software such as Voxelgeo, BCI, RM, DFM and AP, the authors have made a comprehensive analysis of the lateral variation of reservoir parameters in the Upper Shazu bed of the third member of the Palaeogene Funing Formation, and compiled the thickness map of the Shazu bed. Also, with the data from ANN, BCI and the abstracting method for seismic characteristic parameters in combination with the structural factors, the authors have tried the multi-parameter and multi-method prediction of petroleum, delineated the potential oil and gas areas and proposed two well sites. The prediction of oil and gas for Well JB2 turns out to be quite successful.

Seamless Prediction in China: A Review

Seamless prediction is a weather-climate integrated prediction covering multiple time scales that include days,weeks,months,seasons,years,and decades.Seamless prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years,which have important impacts on economic and social development and important reference value for short-,medium-and long-term decision-making and planning of the country.Therefore,seamless prediction has received widespread attention from the international scientific community recently.As Chinese scientists have also carried out relevant research,this paper reviews the research in China on developments and applications of seamless prediction methods and prediction systems in recent years.Among them,the main progress of seamless prediction methods studies is reviewed from four aspects:short-and medium-range weather forecasting,subseasonal-to-seasonal,seasonal-to-interannual,and decadal climate prediction.In terms of development and application of seamless prediction systems,the main achievements made by meteorological operational departments,scientific institutes,and universities in China in recent years are reviewed.Finally,some of the issues in seamless prediction that need further study are discussed.

Multiple sclerosis：integration of modeling with biology,clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome

Multiple Sclerosis（MS） is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.

Application of Question and Answering on Virtual Human Dialogue:a Review and Prediction

Nowadays,virtual human(VH) is becoming a hot research topic in virtualization.VH dialogue can be categorized as an application of natural language processing(NLP) technology,since it is relational to question and answering(QA) technologies.In order to integrate these technologies,this paper reviews some important work on VH dialogue,and predicts some research points on the view of QA technologies.

Anti-Windup Control Strategy of Drive System for Humanoid Robot Arm Joint

To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.

Identification of working conditions and prediction of FeO content in sintering process of iron ore fines

The iron oxide(FeO)content had a significant impact on both the metallurgical properties of sintered ores and the economic indicators of the sintering process.Precisely predicting FeO content possessed substantial potential for enhancing the quality of sintered ore and optimizing the sintering process.A multi-model integrated prediction framework for FeO content during the iron ore sintering process was presented.By applying the affinity propagation clustering algorithm,different working conditions were efficiently classified and the support vector machine algorithm was utilized to identify these conditions.Comparison of several models under different working conditions was carried out.The regression prediction model characterized by high precision and robust stability was selected.The model was integrated into the comprehensive multi-model framework.The precision,reliability and credibility of the model were validated through actual production data,yielding an impressive accuracy of 94.57%and a minimal absolute error of 0.13 in FeO content prediction.The real-time prediction of FeO content provided excellent guidance for on-site sinter production.

A prediction of the acoustic field of a vibrating box by FEM-BEM

A method based on Holmholtz equation is presented to predict thc acoustic radiation of a vibrating box by combining the finite clement method (FEM) with boundary element method (BEM). The prediction formulation is valid for all frequencies if an extra equation is added. The acoustic field of a vibrating box has been calculated and measured. The effectiveness of the method has been proved by test results.

Traffic Prediction in 3G Mobile Networks Based on Multifractal Exploration

Traffic prediction plays an integral role in telecommunication network planning and network optimization. In this paper, we investigate the traffic forecasting for data services in 3G mobile networks. Although the Box-Jenkins model has been proven to be appropriate for voice traffic （since the arrival of calls follows a Poisson distribution）, it has been demonstrated that the Internet traffic exhibits statistical self-similarity and has to be modeled using the Fractional AutoRegressive Integrated Moving Average （FARIMA） process. However, a few studies have concluded that the FARIMA process may fail in modeling the Internet traffic. To this end, we conducted experiments on the modeling of benchmark Internet traffic and found that the FARIMA process fails because of the significant multifractal characteristic inherent in the traffic series. Thereafter, we investigate the traffic series of data services in a 3G mobile network from a province in China. Rich multifractal spectra are found in this series. Based on this observation, an integrated method combining the AutoRegressive Moving Average （ARMA） and FARIMA processes is applied. The obtained experimental results verify the effectiveness of the integrated prediction method.

A modeling system for drinking water sources and its application to Jiangdong Reservoir in Xiamen city

Drinking water sources are highly valued by authorities for safeguarding the life of a city. Models are widely applied as important and effective tools in the management of water sources. However, it is difficult to apply models in water source management because water managers are often not equipped with the professional knowledge and operational skills necessary for making use of the models. This paper introduces a drinking water source simulation and prediction system that consists of a watershed model, a hydrological model and a water quality model. This system provides methods and technical guidance for the conventional management of water sources and emergency water event response. In this study, the sub-models of the system were developed based on the data of the Jiangdong Reservoir in Xiamen, and the model validation was based on local monitoring data. The hydrological model and water quality model were integrated by computer programming, and the watershed model was indirectly integrated into the system through a network platform. Furthermore, three applications for Jiangdong Reservoir water protection utilizing the system were introduced in this paper, including a conventional simulation, an emergency simulation, and an emergency measures evaluation.