Search

Non-null tests are more versatile than null tests and could give fast, generic tests with sufficient performance. The non-null testing methods are able to measure aspheric surfaces with substantial deviations from a reference sphere. The ray will return along a different path and interfere with a separate reference ray in a non-null arrangement. The retrace error occurs because of the significant disparity between the aspheric and the reference. To correct retracing errors and replicate aspheric fabrication imperfections, complex systems modelling and simulations are required. We propose a ray-tracing-based technique for simulating the non-null interferometer that estimates the optical path differences by tracking lasers across the reference and testing paths. An error correction method is used by ray trace analysis to correct the retracing errors in non-null tests of aspheric surfaces. Experiments were conducted to demonstrate the effectiveness of this strategy. The result of the study shows that the proposed system successfully and effectively rectifies the retracing error compared to other practical findings and computer simulation methods. It is more versatile, faster, and provides adequate aspheric assessment performance. [ABSTRACT FROM AUTHOR]

Accurate day-ahead wind power prediction is the basis for scheduling next-day generation in power systems with a high proportion of wind power. In this study, to overcome the adverse effects of changeable weather conditions and inaccurate numerical weather prediction data on day-ahead wind power prediction, a novel ensemble model considering multiple indicators combined with error correction was proposed. Four representative models that are frequently used for day-ahead wind power forecasting served as benchmark models. Then, considering the mean and variance of prediction errors, the nondominated sorting genetic algorithm was utilized to assemble the forecast outcomes from four benchmark models. Lastly, a statistics-based algorithm for error correction that considered temporal correlation was proposed to enhance prediction accuracy. The effectiveness of the proposed model was verified using data from a real wind farm. In a month of performing the successive day-ahead prediction task, the average errors of the proposed ensemble model were 0.1301 for the root mean square error and 0.1076 for the mean absolute error, which were 1.89%–14.52% and 2.62%–15.54% lower, respectively, than those of other models, including four other individual models and three ensemble models. Through error correction, the average forecast errors decreased to 0.1182 for the root mean square error and 0.0915 for the mean absolute error, indicating 9.14% and 14.96% reductions, respectively. • A novel and feasible framework for day-ahead wind power prediction using NWP data was proposed. • An ensemble model considering multiple indicators was proposed to reduce the uncertainty of prediction. • A statistics-based error correction method considering temporal correlation was proposed. • The effectiveness of the proposed model was verified in a month of successive day-ahead prediction task. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM 10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM 10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM 10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM 10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Recent results have shown that effects of pictorial illusions in grasping may decrease over the course of an experiment. This can be explained as an effect of sensorimotor learning if we consider a pictorial size illusion as simply a perturbation of visually perceived size. However, some studies have reported very constant illusion effects over trials. In the present paper, we apply an error-correction model of adaptation to experimental data of N=40 participants grasping the Müller-Lyer illusion. Specifically, participants grasped targets embedded in incremental and decremental Müller-Lyer illusion displays in (1) the same block in pseudo-randomised order, and (2) separate blocks of only one type of illusion each. Consistent with predictions of our model, we found an effect of interference between the two types when they were presented intermixed, explaining why adaptation rates may vary depending on the experimental design. We also systematically varied the number of object sizes per block, which turned out to have no effect on the rate of adaptation. This was also in accordance with our model. We discuss implications for the illusion literature, and lay out how error-correction models can explain perception-action dissociations in some, but not all grasping-of-illusion paradigms in a parsimonious and plausible way, without assuming different illusion effects. [ABSTRACT FROM AUTHOR]

Accurate tax forecast is very important to carry on the macroscopic regulation efficiently under the market economy. However, experience shows that it is very difficult to improve the accuracy of forecasting by setting up a single forecasting model. This article describes the deficiencies of the present forecasting methods and puts forward a new approach to improve the accuracy of prediction by introducing error correction. First, this paper carries out initial prediction of tax revenue by using the LS-SVR model. Second, the accelerated translation transform and weighting mean value generating transform are introduced to process the error sequence. On the basis of the processed data, the error predictive method based on data transformational GM(1,1) is constructed and predicts the subsequent error. Third, the correction of preliminary prediction values is calibrated. The case study based on Chinese tax revenue during the last 30 years shows that the presented approach improves the accuracy of forecasting significantly compared with the prediction accuracy before correction, and then the validity of the model is verified. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to improve the performance of deterministic high resolution rainfall forecasts caused by severe storms by merging an extrapolation radar-based scheme with a storm-scale Numerical Weather Prediction (NWP) model. Effectiveness of Multi-scale Tracking and Forecasting Radar Echoes (MTaRE) model was compared with that of a storm-scale NWP model named Advanced Regional Prediction System (ARPS) for forecasting a violent tornado event that developed over parts of western and much of central Oklahoma on May 24, 2011. Then the bias corrections were performed to improve the forecast accuracy of ARPS forecasts. Finally, the corrected ARPS forecast and radar-based extrapolation were optimally merged by using a hyperbolic tangent weight scheme. The comparison of forecast skill between MTaRE and ARPS in high spatial resolution of 0.01° × 0.01° and high temporal resolution of 5 min showed that MTaRE outperformed ARPS in terms of index of agreement and mean absolute error (MAE). MTaRE had a better Critical Success Index (CSI) for less than 20-min lead times and was comparable to ARPS for 20- to 50-min lead times, while ARPS had a better CSI for more than 50-min lead times. Bias correction significantly improved ARPS forecasts in terms of MAE and index of agreement, although the CSI of corrected ARPS forecasts was similar to that of the uncorrected ARPS forecasts. Moreover, optimally merging results using hyperbolic tangent weight scheme further improved the forecast accuracy and became more stable. [ABSTRACT FROM AUTHOR]

Carbon price forecasting is an important component of a sound carbon price market mechanism. The accurate prediction of carbon prices is an active topic of research. However, many previous studies have focused on the application of a single model, ignoring the application of combination strategies. In this study, a hybrid forecasting system that includes error correction strategy and divide-conquer strategy is designed to predict the carbon price series accurately. Specifically, the main framework of this article comprises four modules. Data preprocessing module of the divide and conquer strategy is proposed. Next, the optimization module uses a multi-objective grasshopper optimization algorithm to enhance the performance of the prediction module. Then, the error correction module predicts the error sequence and corrects the model results. To verify the performance of the established hybrid forecasting system, experiments were performed using two real carbon price series from China and European Union emissions trading schemes, and the results showed that the mean absolute percentage errors of the system were 2.7793% and 0.6720%, respectively, which are better than the other benchmark methods considered. Moreover, it was proved that the designed forecasting system provides a new, effective, and feasible solution for carbon price forecasting. • An advanced preprocessing strategy is applied to process the signal. • ORELM model is improved by multi-objective optimization strategy. • Datasets for different regions are used in the experiment. • Multiple evaluation metrics for testing system performance. [ABSTRACT FROM AUTHOR]

Abstract: A star centroiding error is a critical error in determining the accuracy of star sensors. As an important component of the systematic centroiding errors, the S-curve error is known for its well-regulated sine shape in a period of a pixel. On the basis of the frequency domain analysis, the analytical expression of an S-curve error is given in this paper. Accordingly, the sources resulting in the S-curve error are comprehensively investigated in this study along with their specific role in determining the characteristics of the S-curve error. Experiments are carried out using a real star sensor with the help of a precise turning table and a starlight simulator. The S-curve error data are collected, and a corresponding correction model in the form of a sine function is developed on the basis of the analytical expression. The correction of the S-curve error is well verified by the additional collected test data in the entire field of view (FOV) with an improvement of at least 35.7% in the star centroiding accuracy. Consequently, the calibration accuracy of the star sensor is also improved by 31.7% after the S-curve error correction. Further experiments are performed to investigate the S-curve errors of stars having saturated pixel energy, and the results of these experiments agree with those of the simulations to a large extent. All the analytical and experimental results indicate that the S-curve error has significant regularity and can be corrected using the proposed method. [Copyright &y& Elsevier]

To achieve real-time simulations for flexible multibody dynamics (FMBD) systems, we suggest data-driven modeling based on deep neural networks (DNNs). While a DNN can represent system dynamics accurately, two main factors of FMBD systems require demanding computational costs for training a DNN. One is a fine discretization of flexible bodies, which produces a large number of training data. The other is the nonlinearity of FMBD, which requires train DNN models to have numerous weight and bias parameters. To overcome these difficulties, we propose a data-driven modeling algorithm for training a DNN efficiently that consists of two steps. First, sets of randomly chosen coarse data sequentially train a DNN model. This helps speed up the training process, even for highly parametrized DNNs. At some point, the model no longer improves, and introducing an error correction step increases the performance of the model. The proposed algorithm is easy to employ and utilizes an efficient size of training data while achieving high performance of the DNN as demonstrated by numerical examples. • The significance of real-time simulations for FMBD problems is discussed. • A training framework for DNNs for FMBD is proposed to achieve real-time simulations. • Numerical results demonstrate the proposed method yields accurate FMBD solutions. [ABSTRACT FROM AUTHOR]

• Challenge of WRCC forecasting is the characteristics of nonlinear multiple indicators. • A feedforward neural network based on normalization and error correction is proposed. • Units and values of multiple indicators are normalized simultaneously. • An error correction method is adopted to improve the effectiveness of proposed model. • Final calibrated model is expressed as an equation rather than a programming language. The water resources carrying capacity (WRCC) is fundamental in aiding sustainable socioeconomic regional development, and increasing attention is being paid to WRCC forecasting. The challenge of WRCC forecasting lies in the complex characteristics of nonlinear multiple indicator time series data. To address this problem, this study proposed a feedforward neural network (FNN) based on normalization and error correction for WRCC forecasting. Firstly, units and values of multiple indicators for use in the WRCC were normalized simultaneously to allow the data to be treated as a single equivalent indicator. Thus, the high-dimensional forecasting model was simplified to a low-dimensional model. To improve the effectiveness of the model, an error correction method was then adopted to correct forecasted WRCC values according to similar corresponding sample values. Two simple types of structured FNN were used to address the overfitting problem, and the bipolar sigmoid function was used as the activation function of hidden nodes. The final calibrated model was expressed as an equation rather than a programming language, thus making it easier to use. Yantai, a city in Shandong Province, was selected as a case study to validate this proposed method. Results showed that the mean relative absolute errors of these two simplified FNN models are 1.23% and 1.18%, respectively. Compared to other models, this model has been shown to be feasible and simple to use for WRCC forecasting. [ABSTRACT FROM AUTHOR]

• An error correction method based on dynamic mode decomposition is proposed. • Developed an extreme value constraint method to further correct outliers. • The proposed method does not depend on any previous assumptions. • The proposed method can improve the prediction accuracy of different models. Accurate short-term load forecasting (STLF) is an important basis for daily dispatching of the power grid, but the non-stationary characteristics of the load series add to the challenge of this task. Many researchers have been working to improve the accuracy and speed of forecasting models, but stability is equally important. This paper develops a forecasting method based on error correction using dynamic mode decomposition (DMD) for STLF, including data selection, error forecasting, and error correction. In the data selection stage, three types of data are selected as input data of the model, including previous day data, same day data in previous week and similar day data obtained by grey relational analysis (GRA). In the error forecasting stage, the data driving characteristics of the DMD algorithm is used to capture the potential spatiotemporal dynamics of error series, thereby realizing the error forecasting. In the error correction stage, on the basis of combining the forecasting results of load and error, an extreme value constraint method (EVCM) is developed to further correct the load demand series. Based on the load data of different regions, this paper selects different performance indicators, such as MAPE, MAE, RMSE, Variance and direction accuracy (DA), to prove that the proposed method has the advantages of accuracy and stability. [ABSTRACT FROM AUTHOR]