DSM Reballing Ball Height Prediction Model

Air cushion furnace is indispensable equipment for the production of high quality strip, and it is significant to national economy. The flotation height is a key factor to the quality and efficiency of the product. However, the current prediction models can merely predict the flotation height of strip in air cushion furnace at single working state. The precision of prediction model is inaccurate at the circumstance of low flotation height. To solve the above problem, firstly, this paper proposes a framework which can predict the flotation height of strip under both stable and vibration states. The framework is composed of the hard division model and prediction model. Secondly, a hard division method is proposed based on clustering which combines stacked denoising autoencoder and floating process knowledge. Thirdly, a parallel hybrid flotation height prediction model is proposed, which can provide desirable prediction results at the circumstance of low flotation height. Finally, the LSSVR model is used to predict the maximum and minimum flotation height of strip at vibration state. The experimental results show that the framework can accurately divide the stable and vibration states of the strip and can accurately predict the flotation height of the strip under the stable and vibration states. The research contents of this paper lay an important theoretical foundation for the precise process control in air cushion furnace.

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Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan

Water ◽

10.3390/w10121800 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1800 ◽

Cited By ~ 4

Author(s):

Chih-Chiang Wei ◽

Chia-Jung Hsieh

Keyword(s):

Prediction Model ◽

Wave Height ◽

Prediction Accuracy ◽

Complete Information ◽

Rogue Waves ◽

Superior Performance ◽

Efficiency Coefficient ◽

Height Prediction ◽

Relative Errors ◽

Sea Area

In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in the northeastern sea area of Taiwan to conduct long-term monitoring and collect oceanographic data. However, records have often become lost and the buoys have suffered other malfunctions, causing a lack of complete information concerning wind-generated waves. The goal of the present study was to determine the feasibility of using information collected from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of data from both buoys are discussed herein. This study established a prediction model, and two scenarios were used to assess the performance: Scenario 1 included information from the adjacent buoy and Scenario 2 did not. An artificial neural network was used to establish the wave height prediction model. The research results demonstrated that (1) Scenario 1 achieved superior performance with respect to absolute errors, relative errors, and efficiency coefficient (CE) compared with Scenario 2; (2) the CE of Longdong (0.802) was higher than that of Guishandao (0.565); and (3) various types of typhoon paths were observed by examining each typhoon. The present study successfully determined the feasibility of using information from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of both buoys were also discussed.

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A New Model of Adult Height Prediction Validated in Boys with Constitutional Delay of Growth and Puberty

Hormone Research in Paediatrics ◽

10.1159/000499712 ◽

2019 ◽

Vol 91 (3) ◽

pp. 186-194 ◽

Cited By ~ 1

Author(s):

Thomas Reinehr ◽

Elisa Hoffmann ◽

Juliane Rothermel ◽

Theresa Johanna Lehrian ◽

Jürgen Brämswig ◽

...

Keyword(s):

Prediction Model ◽

Adult Height ◽

Bone Age ◽

New Model ◽

Significant Difference ◽

Constitutional Delay ◽

Height Prediction ◽

Height Predictions ◽

Bp Model ◽

Independent Cohort

Background: For children with retarded bone ages such as in constitutional delay of growth and puberty (CDGP) there are no specific methods to predict adult height based on bone age. Widely used methods such as Bayley-Pinneau (BP) tend to overestimate adult height in CDGP. Objective: We aimed to develop a specific adult height prediction model for teenage boys with retarded bone ages >1 year. Methods: Based on the adult heights of 68 males (median age 22.5 years) a new height prediction model was calculated based on 105 height measurements and bone age determinations at a median age of 14.0 years. The new model was adapted for the degree of bone age retardation and validated in an independent cohort of 32 boys with CDGP. Results: The BP method overestimated adult height (median +1.2 cm; p = 0.282), especially in boys with a bone age retardation ≥2 years (median +1.6 cm; p = 0.027). In the validation study, there was no significant difference between adult height and predicted adult height based on the new model (p = 0.196), while the BP model led to a significant overestimation of predicted adult height (median +4.1 cm; p = 0.009). Conclusions: The new model to predict adult height in boys with CDGP provides novel indices for height predictions in bone ages >13 years and is adapted to different degrees of bone age retardation. The new prediction model has a good predictive capability and overcomes some of the shortcomings of the BP model.

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An age-independent individual tree height prediction model for boreal spruce–aspen stands in Alberta

Canadian Journal of Forest Research ◽

10.1139/x94-169 ◽

1994 ◽

Vol 24 (7) ◽

pp. 1295-1301 ◽

Cited By ~ 22

Author(s):

Shongming Huang ◽

Stephen J. Titus

Keyword(s):

Prediction Model ◽

Nonlinear Regression ◽

Prediction Method ◽

Tree Height ◽

White Spruce ◽

Data Set ◽

Individual Tree ◽

Permanent Sample Plots ◽

Height Prediction ◽

Age Independent

This study presents an individual tree height prediction model for white spruce (Piceaglauca (Moench) Voss) and trembling aspen (Populustremuloides Michx.) grown in boreal mixed-species stands in Alberta. The model is based on a three-parameter Chapman–Richards function fitted to data from 164 permanent sample plots using the parameter prediction method. It is age independent and expresses tree height as a function of tree diameter, tree basal area, stand density, species composition, site productivity, and stand average diameter. This height-prediction model was fitted by weighted nonlinear regression for spruce and unweighted nonlinear regression for aspen. Almost all estimates of parameters were significant at α = 0.05 and model R2-values were high (0.9192 for white spruce and 0.9087 for aspen). No consistent underestimate or overestimate of tree heights was evident in plots of studentized residuals against predicted heights. The model was also tested on an independent data set representing the population on which the model was to be used. Results showed that the average prediction biases were not significant at α = 0.05 for either species, indicating that the model appropriately described the data and performed well when predictions were made.

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Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

Applied Sciences ◽

10.3390/app112411949 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11949

Author(s):

Natago Guilé Mbodj ◽

Mohammad Abuabiah ◽

Peter Plapper ◽

Maxime El Kandaoui ◽

Slah Yaacoubi

Keyword(s):

Machine Learning ◽

Additive Manufacturing ◽

Prediction Model ◽

Travel Speed ◽

Bead Geometry ◽

Layer By Layer ◽

Evolutionary Trend ◽

Deposition Parameters ◽

Layer Deposition ◽

Height Prediction

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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