Papers by Keyword: Prediction

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Authors: Chao Rong Wu, Wen Shen Duan, Rong Cai Zheng
Abstract: Rock of Xujiahe formation in Sichuan basin is characterized by very low porosity and permeability, forecast the spatial distribution of fractured reservoir is the key of success in exploration. Combine logging and seismic data, analysis multi-attributes, optimizing post-stack seismic data processing method, we selected amplitude, average energy, chaotic reflection and amplitude weighted instantaneous frequency (AWIF) to study seismic response. The fractured reservoir have weak amplitude, low average energy, low AWIF and high chaotic reflection. Then with the aid of 3D visual technology, research data set, and recognize that some data, like amplitude and impedance etc, should emphases low value, others like chaotic reflection and auto-fault extraction(AFE) etc, should stand out high value. Base on these, form a suit of technology predicting fractured reservoir by using 3D visualization method. Take amplitude, chaotic reflection and AFE data as case, depict the spatial distribution of fractured reservoir. The results can clearly exhibit the spatial distribution of reservoir
Authors: Chien Hsun Chen, Chin Fa Chen, Ming Hua Hsu, Iuon Chang Lin
Abstract: Volleyball is a popular exercise. There are not only lots of sports enthusiasts but also many professional athletes. In a variety of tactics and globalization of volleyball sport, the match situation becomes complex and intense a lot in the relative. However, for the professional athletes, the focus of training is still just the specific skills and fitness training, so only doing the traditional training courses will make the athletes more difficult to get the winning. Therefore, in this paper, a new training conception is proposed to enhance the volleyball players successful blocking rate by neural network.
Authors: Juan Li
Abstract: To effectively predict cascading failure in power system, a cascading failure prediction method in power system based on multi-agent and hybrid genetic algorithm is constructed. A cascading failure prediction procedure in power system was established by multi-agent and hybrid genetic algorithm to investigate the emergent behaviors of cascading failures and to further study the prediction and defense of cascading failures. Finally, the cascading failure prediction simulation system of power system based on this method was demonstrated and validated by Flexsim software. The result showed that the proposed method was available, and can provided guidance for avoiding and predict cascading failure in power system, and support for stable performance in power system.
Authors: Ji Hong Yan, Chao Zhong Guo, Xing Wang, De Bin Zhao
Abstract: This paper proposed a neural network (NN) based remaining useful life (RUL) prediction approach. A new performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, back propagation neural networks are trained for RUL prediction, and average of the networks’ outputs is considered as the final RUL in order to overcome prediction errors caused by random initiations of NNs. Finally, an experiment is set up based on a Bently-RK4 rotor unbalance test bed to validate the neural network based life prediction models, experimental results illustrate the effectiveness of the methodology.
Authors: Li Dai, Xiong Jun He, Feng Shen
Abstract: It is necessary to research on the long term structural behavior of GFRP bars in concrete by limited samples. The two models, Arrhenius Equation and Fick’s Law were compared to form a long-term durability prediction thinking. Based on the work and ideas of the prior researchers, a detailed derivation of the iterative equation on the Bayesian prediction was conducted. Especially, a new likelihood function was set up to solve the long term behavior prediction of GFRP bars in concrete with less information. Then a new and effective solution method was developed. The residual strength behavior of GFRP bars in concrete beams after seven years’ environmental exposure was analyzed. The results showed that the theoretical predictions and experimental data were very close and theoretical prediction model was reasonable.
Authors: Li Ying Wang
Abstract: The population size and structure is an important factor that affects economic and social development. In this thesis, MATLAB is used to build the relational graph between total population and year according to the total population in the statistical bulletin issued by Jilin Province Statistical Bureau; the grey GM (1,1) model is built with the population size between 2004 and 2013 as the original sequence; the statistical software SPSS18.0 is also used to solve grey GM(1,1) model and it is obtained that the total population in Jilin Province will grow slowly in the future ten years; finally, the rationalization proposal for controlling the population increase of Jilin Province is put forward.
Authors: Mohammadjavad Zeinali, Saiful Amri Mazlan, Abdul Yasser Abd Fatah, Hairi Zamzuri
Abstract: Magnetorheological damper is a controllable device in semi-active suspension system to absorb unwanted movement. The accuracy of magnetorheological damper model will affect performance of the control system. In this paper, a combination of genetic algorithm (GA) and adaptive-network-based fuzzy inference system (ANFIS) approaches is utilized to model the magnetorheological damper using experimental results. GA algorithm is implemented to modify the weights of the trained ANFIS model. The proposed method is compared with ANFIS and artificial neural network (ANN) methods to evaluate the prediction performance. The result illustrates that the proposed GA-weighted adaptive neuro-fuzzy model has successfully predicted the magnetorheological damper behaviour and outperformed other compared methods.
Authors: Fa Chao Li, Ke Na Zhang
Abstract: Regression analysis, as an important branch of statistics, is an effective tool for scientific prediction. Genetic algorithm is an optimization search algorithm in computational mathematics. In this paper, a new regression model named quasi-linear regression model is established. Further, its implementation method is introduced in detail. Then by taking the population development of Hebei province as an example, we conduct the fitting problem and short-term prediction. Moreover, we compare the fitting effect and the prediction results of two models.
Authors: Wei Zhang, Zhi Ping Li, Yu Wang
Abstract: The steam flooding is one common way used to enhance oil recovery of heavy oil reservoir. It is difficult to use conventional treatment on steam flooding analysis because of numerous effective factors, complex relationships and no certain mapping relation among those factors. This paper applies grey relationship method to analyze correlative degree of the factors which effect on steam flooding, and then selects steam absorption effect as the comparative indicator, finally gets a sort of steam flooding effect of different parameters. Each correlation degree has a good match with steam flooding effect, and the higher of correlation degree the better of steam flooding effect. Grey relationship is an effective, fast and accurate method for appraising steam flooding effect.
Authors: Wen Zhan Dai, Qi Feng Zheng
Abstract: In this paper, based on the basic theory of GM(1,1), we present a new method based on cosine function transform the discrete data sequence disposed through the standardization processing to improve the smoothness, first we theoretically proved that this transformation can improve smoothness of the original data sequence after a certain standardization, and more effective than some former method, an example is used to demonstrate the effectiveness of this method in the last of this paper.
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