scholarly journals High-precision Online Prediction Model of Plate Crown Based on Multi-factor Interaction Mechanism

2020 ◽  
Author(s):  
Li ZhiQiang ◽  
YuanMing Liu ◽  
Tao Wang ◽  
Ignatov Aleksei Vladimirovih ◽  
QingXue Huang
Keyword(s):  
Prediction Model ◽  
High Precision ◽  
Prediction Models ◽  
Interaction Mechanism ◽  
Coupled Model ◽  
Correction Coefficient ◽  
New Model ◽  
Calculation Results ◽  
Factor Interaction ◽  
Very High

Abstract The crown is a key quality index of strip and plate. Up to now, many plate crown prediction models have been established, however, the calculation accuracy of these traditional models is not very high. This is because these models only consider the effect of various influencing factors on the outlet plate crown under the basic process parameters, but these factors are not independent of each other. Therefore, in order to improve the accuracy of the plate crown prediction model, a high-precision prediction model of plate crown is established by introducing the evaluation coefficient that represents the influence of single factor on plate crown and the correction coefficient that represents the effect of factor a on the evaluation coefficient of factor b. This paper takes the steel SPHC as an example, calculates the plate crown without considering the correction coefficient and the plate crown considering the correction coefficient. Then, the two calculation results are compared with the calculation results of the coupled model and the accuracy of the new model is improved after considering the correction coefficient. In general, compared with the traditional crown prediction model, the accuracy of the new model is greatly improved.

New Perceptron Model Using Random Bitstreams

1995 ◽  
Vol 7 (2) ◽  
pp. 280-283
Author(s):  
Eel-wan Lee ◽  
Soo-Ik Chae
Keyword(s):  
High Precision ◽  
New Model ◽  
Vlsi Technology ◽  
Very High

A very high precision is needed to implement the adder using stochastic computation (or pulse arithmetic) in modern VLSI technology. In this paper we propose a new model of perceptron using random bitstreams that alleviates this problem.

scholarly journals Discussion of a Coupled Strength Attenuation Model for GFRP Composites in Hydrothermal Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-6
Author(s):  
Wei Chen ◽  
Yongxin Yang ◽  
Biao Li
Keyword(s):  
Experimental Data ◽  
Coupled Model ◽  
Gfrp Composites ◽  
New Model ◽  
Attenuation Model ◽  
Fiber Reinforced Polymer Composites ◽  
Calculation Results ◽  
Temperature Parameter ◽  
Attenuation Models ◽  
Hydrothermal Environments

The existing attenuation models for the durability of FRP (fiber-reinforced polymer) composites in hydrothermal environments were compared, and a new coupled strength attenuation model with a temperature parameter was proposed in this paper. A series of durability experiments on GFRP sheets in hydrothermal environments were conducted to validate the accuracy and rationality of the new model. A comparison between experimental data and the calculation results of the coupled model indicated that the new model can fit better with the experimental data and effectively reflect the convergence phenomenon in the strength attenuation of GFRP in hydrothermal environments. With a temperature parameter included, the new model can better predict the service life of GFRP composites at different aging temperatures. According to the coupled attenuation model proposed in this paper, a concept and calculation method of the slow-aging time point are put forward, which can be convenient for the evaluation and design of GFRP structures with long-term durability.

scholarly journals Research on a Grey Prediction Model of Population Growth Based on a Logistic Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Mingyu Tong ◽  
Zou Yan ◽  
Liu Chao
Keyword(s):  
Population Growth ◽  
Prediction Model ◽  
Growth Model ◽  
Prediction Models ◽  
Least Squares Method ◽  
Grey Prediction ◽  
Growth Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Advantages And Disadvantages

The classical population growth models include the Malthus population growth model and the logistic population growth model, each of which has its advantages and disadvantages. To address the disadvantages of the two models, this paper establishes a grey logistic population growth prediction model, based on the modeling mechanism of the grey prediction model and the characteristics of the logistic model, which uses the least-squares method to estimate the maximum population capacity. In accordance with the data characteristics of population growth, the weakening buffer operator is used to establish the weakening buffer operator grey logistic population growth prediction model, which improves its accuracy, thus improving the classic population prediction model. Four actual case datasets are used simultaneously, and the two classical grey prediction models are compared. The results of the six evaluation indicators show that the effects of the new model demonstrate obvious advantages. Finally, the new model is applied to the population forecast of Chongqing, China. The prediction results suggest that the population may reach a peak in 2020 and decline in the future. This finding is consistent with the logistic population growth model.

scholarly journals Quadratic Prediction Models for the Performance Comparison of a Marine Engine Fuelled with Biodiesels B5 and B20

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Chedthawut Poompipatpong
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Conventional Technique ◽  
Performance Comparison ◽  
Fuel Property ◽  
Marine Engine ◽  
Time Usage ◽  
Renewable Energy Development ◽  
Calculation Results ◽  
Point To Point

According to Thailand’s renewable energy development plan, biodiesel is one of the interesting alternative energies. In this research, biodiesels B5 and B20 are tested in a marine engine. The experimental results are then compared by using three different techniques including (1) the conventional technique, (2) average of the point-to-point comparisons, and (3) a comparison by using quadratic prediction models. This research aims to present the procedures of these techniques in-depth. The results show that the comparison by using quadratic prediction models can accurately predict ample amounts of results and make the comparison more logical. The results are compatible with those of the conventional technique, while the average of the point-to-point comparisons shows diverse results. These results are also explained on the fuel property basis, confirming that the quadratic prediction model and the conventional technique are practical, but the average of the point-to-point comparison technique presents an inaccurate result. The benefit of this research shows that the quadratic prediction model is more flexible for future science and engineering experimental design, thus reducing cost and time usage. The details of the calculation, results, and discussion are presented in the paper.

scholarly journals A Novel Weighted Fractional GM(1,1) Model and Its Applications

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Qin-Qin Shen ◽  
Quan Shi ◽  
Tian-Pei Tang ◽  
Lin-Quan Yao
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Original Sequence ◽  
New Model ◽  
Practical Applications ◽  
New Information ◽  
Generating Operator ◽  
Best Fitting ◽  
Two Parameters ◽  
Fitting Accuracy

Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

An Epidemic Avian Influenza Prediction Model Based on Google Trends

2019 ◽  
Vol 16 (4) ◽  
pp. 303-310 ◽  
Author(s):  
Yi Lu ◽  
Shuo Wang ◽  
Jianying Wang ◽  
Guangya Zhou ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Avian Influenza ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Google Trends ◽  
Health Providers ◽  
Human Beings ◽  
Success Rates ◽  
New Model ◽  
Regression Approach

The occurrence of epidemic avian influenza (EAI) not only hinders the development of a country's agricultural economy, but also seriously affects human beings’ life. Recently, the information collected from Google Trends has been increasingly used to predict various epidemics. In this study, using the relevant keywords in Google Trends as well as the multiple linear regression approach, a model was developed to predict the occurrence of epidemic avian influenza. It was demonstrated by rigorous cross-validations that the success rates achieved by the new model were quite high, indicating the predictor will become a very useful tool for hospitals and health providers.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

scholarly journals Separate Universe calibration of the dependence of halo bias on cosmic web anisotropy

2020 ◽  
Vol 499 (3) ◽  
pp. 4418-4431 ◽  
Author(s):  
Sujatha Ramakrishnan ◽  
Aseem Paranjape
Keyword(s):  
Dark Matter ◽  
High Precision ◽  
Gaussian Distribution ◽  
Initial Perturbation ◽  
Analytical Framework ◽  
Web Environment ◽  
Wide Range ◽  
Galaxy Assembly ◽  
Very High ◽  
Good Agreement

ABSTRACT We use the Separate Universe technique to calibrate the dependence of linear and quadratic halo bias b1 and b2 on the local cosmic web environment of dark matter haloes. We do this by measuring the response of halo abundances at fixed mass and cosmic web tidal anisotropy α to an infinite wavelength initial perturbation. We augment our measurements with an analytical framework developed in earlier work that exploits the near-lognormal shape of the distribution of α and results in very high precision calibrations. We present convenient fitting functions for the dependence of b1 and b2 on α over a wide range of halo mass for redshifts 0 ≤ z ≤ 1. Our calibration of b2(α) is the first demonstration to date of the dependence of non-linear bias on the local web environment. Motivated by previous results that showed that α is the primary indicator of halo assembly bias for a number of halo properties beyond halo mass, we then extend our analytical framework to accommodate the dependence of b1 and b2 on any such secondary property that has, or can be monotonically transformed to have, a Gaussian distribution. We demonstrate this technique for the specific case of halo concentration, finding good agreement with previous results. Our calibrations will be useful for a variety of halo model analyses focusing on galaxy assembly bias, as well as analytical forecasts of the potential for using α as a segregating variable in multitracer analyses.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

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