RBF Prediction Model Based on EMD for Forecasting GPS Precipitable Water Vapor and Annual Precipitation

2013 ◽  
Vol 765-767 ◽  
pp. 2830-2834 ◽  
Author(s):  
Yan Ping Liu ◽  
Yong Wang ◽  
Zhen Wang
Keyword(s):  
Rbf Neural Network ◽  
Precipitable Water ◽  
Annual Precipitation ◽  
Maximum Deviation ◽  
Atmospheric Sciences ◽  
Precipitation Forecasting ◽  
Mode Function ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Predicted Values

The forecast of precipitations is important in meteorology and atmospheric sciences. A new model is proposed based on empirical mode decomposition and the RBF neural network. Firstly, GPS PWV time series is broken down into series of different scales intrinsic mode function. Secondly, the phase space reconstruction is done. Thirdly, each component is predicted by RBF. Finally, the final prediction value is reconstructed. Next, the model is tested on annual precipitation sequence from 2001 to 2010 in northeast China. The result shows that predictive value is close to the actual precipitation, which can better reflect the actual precipitation change. From 2001 to 2010, the maximum deviation of the predicted values never exceeds 4%. The testing results show that the proposed model can increase precipitation forecasting accuracies not only in GPS PWV but also in annual precipitation.

scholarly journals Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Guohui Li ◽  
Wanni Chang ◽  
Hong Yang
Keyword(s):  
Prediction Model ◽  
Volterra Model ◽  
Forecasting Model ◽  
Temperature Prediction ◽  
Mean Temperature ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Energy Environment ◽  
Predicted Values ◽  
Combined Forecasting

Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model.

scholarly journals A Compound Approach for Monthly Runoff Forecasting Based on Multiscale Analysis and Deep Network with Sequential Structure

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2274 ◽  
Author(s):  
Shi Chen ◽  
Shuning Dong ◽  
Zhiguo Cao ◽  
Junting Guo
Keyword(s):  
Water Resource Management ◽  
Sequential Structure ◽  
Runoff Forecasting ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Runoff Series ◽  
Efficiency And Effectiveness ◽  
Predicted Values ◽  
Monthly Runoff ◽  
Gated Recurrent Unit

Accurate runoff forecasting is of great significance for the optimization of water resource management and regulation. Given such a challenge, a novel compound approach combining time-varying filtering-based empirical mode decomposition (TVFEMD), sample entropy (SE)-based subseries recombination, and the newly developed deep sequential structure incorporating convolutional neural network (CNN) into a gated recurrent unit network (GRU) is proposed for monthly runoff forecasting. Firstly, the runoff series is disintegrated into a collection of subseries adopting TVFEMD, considering the volatility of runoff series caused by complex environmental and human factors. The subseries recombination strategy based on SE and recombination criterion is employed to reconstruct the subseries possessing the approximate complexity. Subsequently, the newly developed deep sequential structure based on CNN and GRU (CNNGRU) is applied to predict all the preprocessed subseries. Eventually, the predicted values obtained above are aggregated to deduce the ultimate prediction results. To testify to the efficiency and effectiveness of the proposed approach, eight relevant contrastive models were applied to the monthly runoff series collected from Baishan reservoir, where the experimental results demonstrated that the evaluation metrics obtained by the proposed model achieved an average index decrease of 44.35% compared with all the contrast models.

scholarly journals Study on Cutting Chip in Milling GH4169 with Indexable Disc Cutter

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3135
Author(s):  
Gensheng Li ◽  
Chao Xian ◽  
Hongmin Xin
Keyword(s):  
Chip Thickness ◽  
Cutting Parameters ◽  
Disc Cutter ◽  
Maximum Deviation ◽  
Feed Speed ◽  
Deviation Rate ◽  
Qualitative Relationship ◽  
Predicted Values ◽  
Deformation Coefficient ◽  
And Control

The study and control for chip have a significant impact on machining quality and productivity. In this paper, GH4169 was cut with an indexable disc milling cutter. The chips corresponding to each group of cutting parameters were collected, and the chip parameters (chip curl radius, chip thickness deformation coefficient, and chip width deformation coefficient) were measured. The qualitative relationship between the chip parameters and cutting parameters was studied. The quadratic polynomial models between chip parameters and cutting parameters were established and verified. The results showed that the chip parameters (chip curl radius, chip thickness deformation coefficient and chip width deformation coefficient) were negatively correlated with spindle speed; chip parameters were positively correlated with feed speed; chip parameters were positively correlated with cutting depth. The maximum deviation rate between measured values and predicted values for chip curl radius was 9.37%; the maximum deviation rate for cutting thickness deformation coefficient was 13.8%, and the maximum deviation rate of cutting width deformation coefficient was 7.86%. It can be seen that the established models are accurate. The models have guiding significance for chip control.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

Assessment of the Efficiency of Aliquat 336+Rice Bran Oil for Separation of Acrylic Acid from Aqueous Solution Using Reactive Extraction

2018 ◽  
Vol 16 (9) ◽  
Author(s):  
Himangshu Mondal ◽  
Kanti Kumar Athankar ◽  
Kailas L. Wasewar
Keyword(s):  
Acrylic Acid ◽  
Rice Bran ◽  
Renewable Resources ◽  
Extraction Efficiency ◽  
Process Development ◽  
Rice Bran Oil ◽  
Reactive Extraction ◽  
Aliquat 336 ◽  
Proposed Model ◽  
Predicted Values

Abstract Biomass is an attractive target in process development for the emerging renewable resources based bio-refinery industry. Due to the ample range of application of acrylic acid, its production through bio-route received more awareness in scientific fraternity. In this view, an attempted was made to study the reactive extraction of acrylic acid with aliquat 336 in rice bran oil. Moreover, Box-Behnken matrix was employed to corroborate the effects of process variables viz. concentration of acrylic acid [CAA]aq, concentration of aliquat 336 [CR4N+Cl], and temperature on the extraction efficiency (η%). In physical extraction, average extraction efficiency was found in the order as: 43.55 > 35.36 > 29.14 at 303 K, 323 K, and 343 K respectively in rice bran oil. The correlation coefficient, R2 = 0.988 % indicates the appropriateness of proposed model to predict the extraction efficiency in terms of independent variables, and the predicted values were found in close agreement with that of experimental results. Further, R2(Pred) = 0.806 is in reasonable agreement with the R2(Adj) = 0.972. The optimum conditions for extraction of acrylic acid using aliquat 336 as an extractant in rice bran oil are [CAA]aq = 0.0.5 (mol/kg); [CR4N+Cl] = 1.98 (mol/kg); temperature = 323 K and the model predicted extraction efficiency 77.5 % was found to be an excellent fit with the experimental value 75 %. Further, number of theoretical stages was found to be 3 and S/F ratio 0.247.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

Prediction of Design Hourly Volume on Rural Roads

2020 ◽  
pp. 036119812096556
Author(s):  
Miloš Petković ◽  
Vladan Tubić ◽  
Nemanja Stepanović
Keyword(s):  
Coefficient Of Determination ◽  
Rural Roads ◽  
The Road ◽  
Annual Average Daily Traffic ◽  
Proposed Model ◽  
Traffic Volumes ◽  
Implementation Costs ◽  
On The Road ◽  
Predicted Values ◽  
Design Hourly Volume

Design hourly volume (DHV) represents one of the most significant parameters in the procedures of developing and evaluating road designs. DHV values can be accurately and precisely calculated only on the road sections with the implemented automatic traffic counters (ATCs) which constantly monitor the traffic volume. Unfortunately, many road sections do not contain ATCs primarily because of the implementation costs. Consequently, for many years, the DHV values have been defined on the basis of occasional counting and the factors related to traffic flow variability over time. However, it has been determined that this approach has significant limitations and that the predicted values considerably deviate from the actual values. Therefore, the main objective of this paper is to develop a model which will enable DHV prediction on rural roads in cases of insufficient data. The suggested model is based on the correlation between DHVs and the parameters defining the characteristics of traffic flows, that is, the relationship between the traffic volumes on design working days and non-working days, and annual average daily traffic. The results of the conducted research indicate that the application of the proposed model enables the prediction of DHV values with a significant level of data accuracy and reliability. The coefficient of determination (R2) shows that more than 98% of the variance of the calculated DHVs was explained by the observed DHV values, while the mean error ranged from 4.86% to 7.84% depending on the number of hours for which DHV was predicted.

Determination of Recrystallization Kinetics Model of 30Cr2Ni4MoV Steel Based on Dislocation Density

2017 ◽  
Vol 263 ◽  
pp. 59-66
Author(s):  
Peng Zhou ◽  
Qing Xian Ma
Keyword(s):  
Dislocation Density ◽  
Model Comparison ◽  
Kinetics Model ◽  
Structure Evolution ◽  
Metallographic Analysis ◽  
Recrystallization Kinetics ◽  
Proposed Model ◽  
Different Temperatures ◽  
Predicted Values ◽  
Experimental Values

A new model to predict the structure evolution of 30Cr2Ni4MoV steel is proposed based on the dislocation density in this research. Hot compression of 30Cr2Ni4MoV steel is carried out on Gleeble 1500 at different temperatures from 1233 K to 1473 K with a strain rate of 0.01 s-1 and the deformed samples are immediately quenched by water to frozen the austenite structure. The recrystallization kinetics model of 30Cr2Ni4MoV steel is successfully established by inverse analysis of the flow curve based on the relation between flow stress and dislocation density. In order to validate the proposed model, comparison between the predicted values and experimental values obtained by metallographic analysis is implemented. It is shown that the predicted results agree with the experimental results well.

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