The Spatiotemporal Prediction Model of Opioids Spread Trend Based on Grey Correlation

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

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Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Mathematical Problems in Engineering ◽

10.1155/2020/8863425 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Haibo Liu ◽

Yujie Dong ◽

Fuzhong Wang

Keyword(s):

Correlation Analysis ◽

Prediction Model ◽

Least Square ◽

Support Vector ◽

Entropy Weight ◽

Grey Correlation ◽

Gas Outburst ◽

Penalty Factor ◽

Kernel Parameter ◽

Grey Correlation Analysis

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

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Using Artificial Intelligence in IC Substrate Production Predicting

10.21203/rs.3.rs-552378/v1 ◽

2021 ◽

Author(s):

Zhifang Liu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Decision Making ◽

Prediction Model ◽

Nonlinear Models ◽

Future Market ◽

Prediction Ability ◽

Grey Correlation ◽

The Future ◽

Grey Correlation Analysis

Abstract Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.

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Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures

Advances in Materials Science and Engineering ◽

10.1155/2017/2062758 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Hui Wang ◽

Haoqi Tan ◽

Tian Qu ◽

Jiupeng Zhang

Keyword(s):

Prediction Model ◽

Influencing Factor ◽

Correlation Method ◽

Asphalt Mixtures ◽

Good Prediction ◽

Modified Asphalt ◽

Grey Correlation ◽

Depth Prediction ◽

Test Conditions ◽

Good Prediction Accuracy

APA rutting tests were conducted for six kinds of asphalt mixtures under air-dry and immersing conditions. The influences of test conditions, including load, temperature, air voids, and moisture, on APA rutting depth were analyzed by using grey correlation method, and the APA rutting depth prediction model was established. Results show that the modified asphalt mixtures have bigger rutting depth ratios of air-dry to immersing conditions, indicating that the modified asphalt mixtures have better antirutting properties and water stability than the matrix asphalt mixtures. The grey correlation degrees of temperature, load, air void, and immersing conditions on APA rutting depth decrease successively, which means that temperature is the most significant influencing factor. The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.

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