Gray prediction theory applications in drill stem failure

The prediction is complex structure and the grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. GM(1, 1) model is one of the frequently used basic model of grey prediction with simple structure. Based on the grey prediction modeling theory and with an analysis of the shortages of GM(1, 1) model, an adaptive multi-variable optimized grey model based on cuckoo search algorithm is proposed, which reconstructs whitening background values and initial conditions in view of the relationship among many related reduction feature information. Several experiments are taken for prediction and analysis, and the results show that the new model has high precision and practicability.

Revealed the strength variation of concrete C30 and C50 after the cold in the initial .Using the gray prediction theory which from the cybernetics to analyze the test results after the initial cold.The results show that the results obtained by the formula is consistent with the experimental results, the residuals are within the control range.The experimental in cold for 120 days is much closer than theoretical values when compared with the residual for 90 days.Research shows that with the increase of initial freezing time, the loss rate of concrete strengthincreased.And the residuals showed a significant increasing trend when the number of freeze-thaw cycles increased.

Based on the statistical data during the period from 2001 to 2004 released by Henan statistical bureau in China, this paper aims to predict the amount of scientists and technicians of Henan province in China by the usage of GM (1, 1) model with the four items. The result of this etechnological policiesmpirical study is that the grey prediction theory can fit the scientists and technicians amount development precisely in Henan province. The accuracy of the prediction result is above 90% and corresponds with a distinction, which the grey theory can meet expectations with small samples or data. Research results show that this model established in this paper could provide valuable information for policy makers in their efforts to make appropriate technological policies.

Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

In case of a carbonate reservoir, conventional techniques such as artificial neural networks and pattern recognition may not perform well in predicting the reservoir potential zones. This paper proposes a new implementation to highlight the anomalous zones of a carbonate reservoir using grey theory. The input is a 3D seismic volume for a square area which contains eight drilled wells of varying producing quality. The grey analysis is carried out for the zone of interest on a trace-by-trace basis. The result is validated on the reservoir main producing layer by utilizing the wells production classification information.

The analysis of the basic characteristics of various research methods is highly needed to predict the residual life of the pipeline accurately, help managers understand the operational risks, and provide a reference for developing pipeline transportation and maintenance inspection plans and anti-corrosion measures. Based on a comprehensive investigation of the existing research on the residual life of the pipeline, this paper finds that the current mainstream life prediction method, based on historical statistical data, has the shortcomings of inconsistent modeling methods, inconsistent basic data, and a lack of comparative evaluation among methods. Moreover, considering the in-depth study of BP neural network modeling, grey theory modeling, time series modeling, and exponential smoothing modeling, optimal prediction models using different methods based on the same historical data are established. These optimal modeling methods are discussed, and the feasible modeling path for the accurate prediction of the pipeline’s residual life is given by comparing the prediction accuracy of each model. In addition, the findings serve as a guide for developing an anti-corrosion strategy by highlighting the contribution of the prediction results of the residual life to pipeline decision-making. By comparison, it is found that the accuracy of the four prediction models is as follows: the grey theory prediction model, the exponential smoothing prediction model, the BP neural network prediction model, and the time series prediction model, from high to low, respectively.

Design of an on-machine gage for diameter measurements: Hading, K.; Sieczka, E.; Kaltenbacher, E.; Boehnloin, A. SPIE — Industrial Applications of Optical Inspection, Metrology and Sensing; 1992 Nov 19; Boston, MA. Bellingham, WA: International Society for Optical Engineering; 1993. 439–446

Due to the hazards caused by Vespa Mandarinia, the public reports collected in Washing-ton State were classified. Therefore, two models were established in this paper: The Grey Pre-diction Model and the Prediction Classification Model. We begin with the Grey Prediction Model to predict the problem of whether a pest can spread over a period of time. Based on the gray scale prediction theory and combining with experts’ identification of the sighting report as the data of Vespa Mandarinia, GM (1,1) model has been established by using the gray scale prediction method.

Gray predicting theory and application of energy consumption of building heat-moisture system

The traditional gray prediction theory requires less sample size and simple modeling, but it ignores new information that is more beneficial to prediction, and it is easy to cause the phenomenon of aging of the prediction model, and the prediction accuracy is not high. Therefore, a more scientific and reasonable GM (1,1) gray prediction model based on metabolism is proposed for the problem of urban electricity forecasting. This method avoids the limitations of local information modeling, and the metabolic processing of removing the oldest data of the original sequence of each prediction result ensures the effectiveness of the prediction sequence. The application of model prediction with Jurong city electricity data and the programming calculation of improved GM(1,1) model with Mtalab verify the practicability.

In the HDFS cloud storage environment, the existing replica strategy is not able to adopt the optimal replica according to the current system environment dynamically, which affects the performance of the Cloud Storage system. This paper aims at the replica selection strategies in the load balancing problem, proposes a Replica Strategy based on grey prediction theory in Cloud Storage and predicts the node load rate of different replicas at a moment, selecting the minimum load node dynamically, eventually to load balance.

It is great significance to forecast the water quality of Poyang Lake in the construction of Poyang Lake Ecological Economic Zone.On the condition of limited historical data,for resolving the prediction,by combining the advantages of both grey prediction and Markov theory,an improved grey-Markov model is established.First the sequence is moved average,and then unbiased grey-Markov prediction is established to imitate the development tendency of the forecast system.Divide system state into five parts by Markov chain in order to inprove the prediction accuracy. By analying an example,comparing the predicted values and real measured values,The results show that the model has a certain accuracy and reliability

Based on the Grey prediction theory, we propose in this paper a two-pass algorithm for the sharpening of images. In the first pass, pixels around edges or boundaries are detected with edge detection mechanism. During the second pass, those pixels detected as around edges or boundaries are adjusted for the purpose of image sharpening, and those non-edge pixels are kept unaltered. With the proposed approach, most of the original information contained in the image can be retained. In the second pass, the magnitude, i.e., the increment or decrement, to be added to those edge pixels has to be determined. Usually, a larger additive can have a better sharpening result. However it can also lead to the saturation of intensity around edge pixels. Aimed to find the maximal additive magnitude automatically, we proposed in this paper the use of a Grey prediction model GM(1,1) so that the condition of over-sharpening in images to be sharpened can be avoided. In addition, a scaling factor can also be used for the adjustment of the additive magnitude in the proposed approach. Extensive experiments on natural images as well as medical images are also given in this paper. As we will see in the experiments, the proposed approach can have a very distinct intensity transition for pixels around edges or boundaries in the sharpened images, which demonstrates the usefulness of the proposed approach.

Among fierce competition, it is imperative for industry to be dedicated to adjusting strategic planning in the related R&D activities. However patent is a defensive weapon to protect R&D achievements and an attack weapon to contain the competitive opponents. The emergence of China economy generates the establishment of many own brands one after another, facilitating that China is gradually transformed from imitator. Regarding comprehensive aspect, if decision makers can use the efficient prediction tools to conduct patent trend that will benefit the establishment of precise R&D strategy. To start from this point, the research studies the patent development trend in China for the last few years. The research applies the empirical analysis to study the model accuracy. In the selection of prediction tools, Grey theory is a prevalent prediction method that can be constructed with limited information and can provide a better prediction advantage for short-term prediction. Additionally, application of Grey prediction method featured with handling non-linear problems, insufficient information sources and small sample. In the meantime, the research adds linear regression, exponential smoothing and moving average and takes some patents in China as background to conduct selection among different prediction models. The results indicate that Grey theory is not only equipped with advantage for short-term prediction but also has considerable accuracy than the other traditional models even statistics increase and is under steady growth of trend.