A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS

Rail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost is relatively high. As a result, the accuracy and efficiency of conventional methods cannot be guaranteed. In this article, an accurate and efficient rail grinding profile prediction method is proposed, in which an interval segmentation approach is proposed to improve the prediction efficiency based on the geometric characteristic of a rail profile. Then, the accurate area integral approach with cubic NURBS is used as the grinding area calculation approach to improve the prediction accuracy. Finally, the normal length index is introduced to evaluate the prediction accuracy. The accuracy and stability of the proposed method are verified by comparing a conventional approach based on a practical experiment. The results demonstrate that the proposed method can predict the rail grinding profile in any grinding pattern with high accuracy and efficiency. Meanwhile, its prediction stability basically agrees with the conventional approach.

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Investigation of mechanical properties for 2.5D woven composites with different weft-layer-numbers by a triple-cell model system

Journal of Industrial Textiles ◽

10.1177/1528083720958046 ◽

2020 ◽

pp. 152808372095804

Author(s):

Junhua Guo ◽

Weidong Wen ◽

Hongjian Zhang ◽

Haitao Cui ◽

Jian Song ◽

...

Keyword(s):

Mechanical Properties ◽

Prediction Accuracy ◽

Cell Model ◽

Computational Cost ◽

Prediction Method ◽

Textile Composites ◽

Woven Composites ◽

Weft Yarn ◽

Triple Cell ◽

Practical Factors

As a new type of textile composites with broad application prospects, it is essential to study the prediction method of the mechanical properties of 2.5 D woven composites (2.5DWC). Currently, the most popular prediction method is to use a representative volume cell (RVC) for numerical simulation, so the reasonableness of RVC determines the prediction accuracy. However, many practical factors are ignored in the traditional periodic unit-cell model (UCM), such as the weft-layer-number (WLN), resulting in low prediction accuracy; while the full-cell model (FCM) in which the surface extrusion effect (SEE) and WLN are considered has the problems of complex modeling and high computational cost. To solve these problems, a triple-cell model (TCM) system is proposed, which includes four RVCs that are applicable to different WLNs, each of which is composed of different sub-cells (surface-cell, transition-cell, and inner-cell) which are categorized according to the characteristics of the actual weft yarn cross-section. Based on the progressive damage method, the stiffness, strength, and damage behavior of 2.5DWC with different WLNs are predicted, and the TCM prediction results are compared with the results of the experiment, the UCM, and the FCM. Compared with the experimental results, the prediction accuracy of the TCM is more than 8% higher than that of the UCM, and the difference between the prediction results of the TCM and FCM is less than 5%. Therefore, the proposed TCM system has the characteristics of high prediction accuracy, relatively simple modeling, and the applicability of any WLN.

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Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

Applied Sciences ◽

10.3390/app11135900 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5900

Author(s):

Yohei Fujinami ◽

Pongsathorn Raksincharoensak ◽

Shunsaku Arita ◽

Rei Kato

Keyword(s):

Traffic Accidents ◽

Computational Cost ◽

Prediction Method ◽

Smooth Curve ◽

Assistance System ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Trajectory Prediction ◽

Left Turn ◽

Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

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Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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Research on Combined Prediction Model for Busy Telephone Traffic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.789 ◽

2014 ◽

Vol 610 ◽

pp. 789-796

Author(s):

Jiang Bao Li ◽

Zhen Hong Jia ◽

Xi Zhong Qin ◽

Lei Sheng ◽

Li Chen

Keyword(s):

Prediction Model ◽

Prediction Accuracy ◽

Low Frequency ◽

Prediction Method ◽

Pso Algorithm ◽

Frequency Component ◽

Least Square ◽

High Frequency Component ◽

Support Vector ◽

Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

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Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

Buildings ◽

10.3390/buildings11100458 ◽

2021 ◽

Vol 11 (10) ◽

pp. 458

Author(s):

Yanan Zhao ◽

Zihan Zang ◽

Weirong Zhang ◽

Shen Wei ◽

Yingli Xuan

Keyword(s):

Temperature Distribution ◽

Relative Error ◽

Prediction Accuracy ◽

Prediction Method ◽

Mobile Sensors ◽

Average Relative Error ◽

Contribution Ratio ◽

Mobile Sensor ◽

Indoor Climate ◽

Indoor Temperature

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

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Direct simulation of non-additive variables on unstructured grids : the case of permeability

10.35662/unine-thesis-2888 ◽

2021 ◽

Author(s):

◽

Pauline Mourlanette

Keyword(s):

Unstructured Grids ◽

Computational Cost ◽

Simulation Method ◽

Breakthrough Curves ◽

Tracer Experiment ◽

Conventional Approach ◽

Geostatistical Simulation ◽

Permeability Heterogeneity ◽

Research Perspectives ◽

Parameters Selection

Uncertainties related to permeability heterogeneity can be estimated using geostatistical simulation methods. Usually, these methods are applied on regular grids with cells of constant size, whereas unstructured grids are more flexible to honor geological structures and offer local refinements for fluid-flow simulations. However, cells of different sizes require to account for the support dependency of permeability statistics (support effect). This work presents a novel workflow based on the power averaging technique. The averaging exponent ω is estimated using a response surface calibrated from numerical upscaling experiments. Using spectral turning bands, permeability is simulated on points in each unstructured cell, and later averaged with a local value of ω that depends on the cell size and shape, but also on the proportion of each facies inside the cell. The method is first illustrated on a synthetic case, with a single facies. The simulation of a tracer experiment is used to compare this novel geostatistical simulation method with a conventional approach based on a fine scale Cartesian grid. The results show the consistency of both the simulated permeability fields and the tracer breakthrough curves. The application to an industrial case with two facies is then presented and shows both consistent permeability fields and computational costs acceptable for the industry. Indeed, the computational cost for several realizations is much lower than the conventional approach based on a pressure-solver upscaling. The method works for the presented cases, but its theoretical ro-bustness can still be improved. A discussion on pressure solver upscal-ing parameters selection and power averaging limits is available in the conclusion, as well as a few research perspectives on multiple facies and non stationary proportions inclusion, the management of anisotropy and the extension to multiphase flow.

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An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽

10.3390/s19132946 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2946 ◽

Cited By ~ 19

Author(s):

Wangyang Wei ◽

Honghai Wu ◽

Huadong Ma

Keyword(s):

Traffic Flow ◽

Prediction Accuracy ◽

Short Term Memory ◽

Smart Cities ◽

Prediction Method ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

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Predicting lean blow-off limit of gas turbine combustors based on Damköhler number and detailed atomization information

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650920920038 ◽

2020 ◽

pp. 095765092092003

Author(s):

Zhonghao Wang ◽

Bin Hu ◽

Aibing Fang ◽

Aiming Deng ◽

Junhua Zhang ◽

...

Keyword(s):

Numerical Simulation ◽

Gas Turbine ◽

Calculation Method ◽

Time Scale ◽

Prediction Accuracy ◽

Drop Size ◽

Prediction Method ◽

Negative Effects ◽

Supply Pressure ◽

Uniform Model

A hybrid lean blow-off prediction method based on Damköhler ( Da) number was proposed in the authors’ previous study. However, the uniform model for fuel drop size distribution cannot fully reflect the actual atomization quality under lean blow-off conditions, which has negative effects on prediction accuracy. In the current study, atomization experiments are conducted under different fuel supply pressure. The atomization quality is described by Rosin–Rammler model and is integrated into numerical simulation. The calculation method of chemical time scale ( τc) is improved by accurately differentiating the inlet and outlet surface of reaction zone. After the improvement, the Da number under lean blow-off conditions mainly lies between 0.3 and 0.8, while under the designing condition, the Da number is about 20. Compared with the former method, the optimized method in the present article can distinguish stable combustion states markedly from lean blow-off states. Through the introduction of detailed atomization information and the improvement of time scale calculation, lean blow-off prediction accuracy in the present work is efficiently improved, which can provide powerful technical support for engineering applications.

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Ensemble of Meta-Models Based on Local Error Measure Using Cross-Validation

Volume 5: 35th Design Automation Conference, Parts A and B ◽

10.1115/detc2009-87279 ◽

2009 ◽

Cited By ~ 2

Author(s):

Dohyun Park ◽

Yongbin Lee ◽

Dong-Hoon Choi

Keyword(s):

Prediction Accuracy ◽

Cross Validation ◽

Polynomial Regression ◽

Computational Cost ◽

Support Vector ◽

Local Error ◽

Test Cases ◽

Meta Model ◽

Lower Accuracy ◽

Cross Validation Error

Many meta-models have been developed to approximate true responses. These meta-models are often used for optimization instead of computer simulations which require high computational cost. However, designers do not know which meta-model is the best one in advance because the accuracy of each meta-model becomes different from problem to problem. To address this difficulty, research on the ensemble of meta-models that combines stand-alone meta-models has recently been pursued with the expectation of improving the prediction accuracy. In this study, we propose a selection method of weight factors for the ensemble of meta-models based on v-nearest neighbors’ cross-validation error (CV). The four stand-alone meta-models we employed in this study are polynomial regression, Kriging, radial basis function, and support vector regression. Each method is applied to five 1-D mathematical examples and ten 2-D mathematical examples. The prediction accuracy of each stand-alone meta-model and the existing ensemble of meta-models is compared. Ensemble of meta-models shows higher accuracy than the worst stand-alone model among the four stand-alone meta-models at all test examples (30 cases). In addition, the ensemble of meta-models shows the highest accuracy for the 5 test cases. Although it has lower accuracy than the best stand-alone meta-model, it has almost same RMSE values (less than 1.1) as the best standalone model in 16 out of 30 test cases. From the results, we can conclude that proposed method is effective and robust.

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A Comparative Assessment of Six Machine Learning Models for Prediction of Bending Force in Hot Strip Rolling Process

Metals ◽

10.3390/met10050685 ◽

2020 ◽

Vol 10 (5) ◽

pp. 685 ◽

Cited By ~ 2

Author(s):

Xu Li ◽

Feng Luan ◽

Yan Wu

Keyword(s):

Prediction Accuracy ◽

Computational Cost ◽

Regression Tree ◽

Prediction Performance ◽

Learning Models ◽

Hot Strip Rolling ◽

Strip Rolling ◽

Bending Force ◽

Hot Strip ◽

Machine Learning Models

In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.

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