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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]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

• This paper presents an error correction method for curved rail profile measurement based on selective focus, without adding additional hardware. • This paper proposes a method for solving the optimal auxiliary plane through the objective function of the overlap between virtual 3D rail and actual rail. Its advantage is that the optimal auxiliary plane can be solved through a single line laser. • Laboratory experiments have shown that the rail wear error has been reduced to 0.1 mm, while vehicle dynamic experiments have shown that the rail wear error has been reduced to within 0.2 mm. • This paper proposes a virtual 3D rail creation method based on the center of the light strip, providing a new insight into future work on rail profile measurement error correction. Rail profile detection is an important means of railway operation and maintenance. Currently, structured light profile measurement technology is mainly used for vehicle-mounted rail profile measurement. The body attitude of the vehicle will change as it passes by a curve, and the laser plane of the rail profile measurement device would be non-perpendicular to the rail in the local longitudinal direction, resulting in errors in rail profile measurement. To address this issue, an error correction method based on selective focus for curved rail profile measurement is proposed. The virtual 3D rail is generated with the section image of a single-beam laser and projected onto the image plane, which is then combined with the projection data of virtual 3D rail and the actual rail from the rail image to build the optimization function. The optimal auxiliary plane perpendicular to the rail's longitudinal direction is then determined. Finally, error correction is achieved by projecting the measured data onto the optimal auxiliary plane. The effectiveness and accuracy of the proposed method are validated through experiments, demonstrating that regardless of the laser plane's relative pitch or yaw motion with respect to the rail, the corrected profile wear measurement error was under 0.10 mm, satisfying the requirement for rail wear measurement accuracy. In this study, the plane perpendicular to the longitudinal direction of the rail was obtained through selective focus, thus providing a new insight into future work on rail profile measurement error correction. [ABSTRACT FROM AUTHOR]

Wind power forecasting can effectively improve the energy utilization efficiency of a power system and ensure its stable operation. In this study, a novel hybrid multistep prediction model, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), the kernel principal component analysis (KPCA), an enhanced arithmetic optimization algorithm (ENAOA), a bidirectional long short-term memory (BILSTM) neural network, and error correction, was designed for short-term wind power forecasting. First, the collected original wind power data were decomposed into multiple intrinsic mode functions (IMFs) through a secondary decomposition composed of the CEEMDAN and VMD, which eliminated the interactions between different components to achieve denoising. Second, the KPCA was adopted to reduce the dimensionality of the multiple IMFs by extracting the principal components, effectively reducing the complexity of the multidimensional IMF data and improving the forecasting efficiency of the proposed prediction model. Subsequently, an ENAOA was proposed based on the Sobol sequence, adaptive T-distribution, and random walk strategy to optimize the BILSTM parameters. Finally, multiple preprocessed components were predicted by the optimized BILSTM, after which error correction was performed to obtain the final prediction results, which could further reduce the forecast error of the designed prediction model. Based on two sets of data collected from a wind farm in northwest China, the simulation results of 1-step, 4-step, 7-step, and 10-step forecasting revealed that compared with other incomplete models, the various algorithms adopted in the hybrid forecasting model reduced the prediction errors to different degrees, significantly enhanced the wind power prediction performance, and validated the effectiveness and feasibility of the proposed model. • Original wind power data is preprocessed through CEEMNAN and VMD. • KPCA is adopted to extract principal components of several IMFs. • Propose an improved ENAOA optimization algorithm through three improvements. • Error correction is employed to further increase the forecast accuracy. • The hybrid multi-step forecasting model greatly reduces the prediction error. [ABSTRACT FROM AUTHOR]

The accurate prediction of wind speed is important in satisfying the demands of power grids. However, the prediction of wind speed is challenging because of its randomness and volatility, especially in multi-step cases. This study proposes a novel multi-step wind speed prediction model combining a Weather Research and Forecasting (WRF) simulation and an error correction strategy. First, the WRF model is adopted to predict the wind speed. Variational Mode Decomposition (VMD) is then employed to mine features of the predicted wind speed using the WRF model. The Principal Component Analysis (PCA) method is next used to extract the main components and remove illusive components. Using these principal components and prediction error as the training dataset, Long Short-Term Memory (LSTM) is applied for error correction. The WRF-VMD-PCA-LSTM model is thus developed for the multi-step prediction of wind speed. In a case study of a wind farm located in Sichuan Province, China, the proposed WRF-VMD-PCA-LSTM model outperforms models to which it is compared. The results reveal that the VMD-PCA method effectively extracts features hidden in the numerical WRF output. The proposed model effectively improves the accuracy of multi-step wind speed prediction. • Features hidden in the numerical weather prediction output are extracted by a hybrid VMD-PCA method. • Propose an error correction strategy to improve the prediction accuracy of numerical weather prediction. • The proposed model is effective in multi-step wind speed prediction. [ABSTRACT FROM AUTHOR]

Optical three-dimensional coordinate measurement using stereo vision has systematic errors that affect measurement quality. This paper presents a scheme for measuring, modelling and correcting these errors. The position and orientation of a linear stage are measured with a laser interferometer while a stereo vision system tracks target points on the moving stage. With reference to the higher accuracy laser interferometer measurement, the displacement errors of the tracked points are evaluated. Regression using a neural network is used to generate a volumetric error model from the evaluated displacement errors. The regression model is shown to outperform other interpolation methods. The volumetric error model is validated by correcting the three-dimensional coordinates of the point cloud from a photogrammetry instrument that uses the stereo vision system. The corrected points from the measurement of a calibrated spherical artefact are shown to have size and form errors of less than 50 μm and 110 μm respectively. A reduction of up to 30% in the magnitude of the probing size error is observed after error correction is applied. • Error in stereo vision 3D measurement is evaluated using a laser interferometer. • Volumetric error model is developed from 3D errors using neural network. • A photogrammetric error correction scheme is presented for verification. • Up to 30% reduction in the magnitude of probing size error was observed. [ABSTRACT FROM AUTHOR]

• This study investigates the (mis)alignment between teachers' beliefs of WCF practice and their actual WCF practice. • The results indicate a degree of alignment between teachers' beliefs and practice but only in certain instances. • Most teachers mistakenly believed they primarily provide direct feedback while providing more indirect feedback in practice. • WCF was more frequent for local than global issues, despite teachers believing they offered more WCF on global issues. • Time constraints, excess workload and perceptions of students' attitude to WCF appear responsible for the misalignment. Written corrective feedback (WCF) refers to the feedback provided by writing teachers on students' writing. While much attention has been given to the (in)effectiveness of WCF on writing accuracy or skills in L2 writing research, there has been relatively less research on whether teachers' beliefs regarding WCF are aligned with their actual correction practices. This study investigates the (mis)alignment between teachers' beliefs of their WCF practice and their actual practice, using questionnaire and interview data from five English writing teachers in a Chinese EFL context who provided WCF on 100 student essays. The results indicate a degree of alignment between teachers' beliefs and practice in certain instances. However, three key areas of misalignment between beliefs and WCF practice were found, namely (1) most teachers mistakenly believed they primarily provide direct feedback while providing more indirect feedback in practice; (2) the teachers believed they often indicated the total number of errors in the margin, despite never doing so in practice, and; (3) WCF on local issues received more attention than global issues, despite teachers believing they offered more feedback on global issues. Contextual issues including time constraints, excess workload and perceptions of students' attitude to WCF were found to result in the eventual misalignment between beliefs and WCF practice. To resolve this issue, it is recommended that students and teachers cooperate to achieve the ultimate goals of written feedback, while in-service teachers should undertake periodic explicit professional training on WCF provision. [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]

Nowadays, multiple-error occurrence because of multiple transient faults is highly probable in digital circuits especially used in outer space environments. Therefore, concerning such applications, the processing systems performing operations such as addition and multiplication are of great importance and should be redesigned, properly. In this paper, some multiple-error resilient carry look-ahead adders are proposed more efficient than the existing designs. In this manner, at first, two new customized fault-tolerant voters are proposed and analyzed to be used for multiple-error correction/detection in the carry look-ahead adders utilizing a distributed partial triple modular redundancy along with a parity prediction scheme. Then, the new voters are exploited and incorporated into two types of grouped carry look-ahead adder architectures to obtain different designs with a compromise between the area overhead and reliability. Simulation results show that, as well as a complete single error correction/detection, the proposed voters lead to 8–15% area overhead reduction in different carry look-ahead adders while increasing multiple-error correction probability up to 17% and 26% utilizing the first and second proposed voters, respectively, compared to the best existing design. Furthermore, the total multiple error correction/detection probability is improved in different adder architectures in many cases. • Two new customized fault-tolerant voters are proposed with different error correction/detection probabilities and overheads. • All single errors inside these voters will be either corrected or detected. • Compared to previous voters in CLAs, proposed voters have higher fault-tolerance quality while requiring lower overheads. • Two different CLAs, simple grouped and two-level grouped are proposed, evaluated, and compared to the best existing design. • Designed 64-bit CLAs can correct or detect multiple errors incurred by 10 concurrent faults with 90% probability. [ABSTRACT FROM AUTHOR]

Abstract Radiation is a cause of errors in electronics systems, especially on those deployed in space. Memories can suffer from different radiation effects including Single Event Upsets and Multiple Cell Upsets. These effects produce corruption of data and may cause a system malfunction. 3D die-stacked memories have been designed to increase bandwidth, reduce latency and limit power consumption. The shielding characteristic of the 3D structure causes a heterogeneous fault tolerance behaviour of the system where some of the inner dies are invulnerable while the outer dies require different protection levels. Orthogonal Latin Square codes are a class of error correcting codes that are modular and can provide different error correction degrees depending on the number of parity bits used. This paper proposed a solution based on current standards to provide dynamic and heterogeneous error correction capabilities to a 3D memory. To do so, Orthogonal Latin Square codes are used. [ABSTRACT FROM AUTHOR]

Abstract In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier to the effective observation of sensors. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix factorization (NMF) and error correction method (S-NMF-EC) is proposed in this paper. Firstly, a cloud-free fused reference image is obtained by a reference image and two or more low-resolution images using the spatial and temporal nonlocal filter-based data fusion model (STNLFFM). Secondly, the cloud-free fused reference image is used to remove the cloud cover of the cloud-contaminated image based on NMF. Finally, the cloud removal result is further improved by error correction. It is worth noting that cloud detection is not required by S-NMF-EC, and the cloud-free information of the cloud-contaminated image is maximally retained. Both simulated and real-data experiments were conducted to validate the proposed S-NMF-EC method. Compared with other cloud removal methods, the results demonstrate that S-NMF-EC is visually and quantitatively effective (correlation coefficients ≥ 0.99) for the removal of thick clouds, thin clouds, and shadows. [ABSTRACT FROM AUTHOR]

Abstract Fast tool servo (FTS) system can accurately manufacture the optical components with complex surfaces or microstructures. Due to the influences of moving mass and time constant of drive unit, the output displacement of FTS system will produce the amplitude attenuation and the phase delay, and the machining error will obviously increase in the situation. In this paper, the error correcting algorithm of FTS system is designed, the lead step number and the lead gain are reasonably planned in the error correcting procedure, and the simulating results indicate that the algorithm is simple and the output displacement of FTS system is precise. The effectiveness of designed error correcting algorithm is experimentally verified in the machining procedure of lens array by using FTS system, the machined surface is consistent with the expectant shape referring to the measuring result, and the depth error is 0.06μm in peak-to-valley value. [ABSTRACT FROM AUTHOR]

Highlights • The VIC simulated and in-situ observed SM data were merged. • This was done mainly using CDF matching approach and OI technique. • The CDF was parameterized and then estimated using kriging technique. • A spatial-temporal correlation error function was proposed for OI technique. • The merged SM product was improved compared to the original data. Abstract In order to obtain an improved soil moisture (SM) dataset at large scale, an advanced SM merging methodology based on error correction methods was constructed to merge the model-based and in-situ observed SM data. The SM datasets in a 0–40 cm soil layer were derived from 10 km × 10 km Variable Infiltration Capacity (VIC) model and 797 in-situ stations, respectively. The merging methodology was conducted grid by grid, and mainly included two parts: bias correction and random error correction. Firstly, the bias correction was performed for the VIC simulations by applying the Cumulative Distribution Function (CDF) matching approach combined with the kriging technique. Secondly, the random error of the VIC simulations was corrected using an Optimal Interpolation (OI) technique based on a spatio-temporal correlation function which was proposed and constructed in this study. Through validations against in-situ observations, the merged SM was evaluated, and the merging errors in each step were analyzed and discussed. The results showed that the merged SM product was improved compared to the original SM data, both temporally and spatially. The SM merging methodology is effective and reliable in combining the accurate but sparse in-situ observations and the continuous VIC simulations. In addition, the spatial mismatch impact on the representativeness of in-situ stations was limited, and the merging errors were mainly produced in the CDF estimation process. The random error information in the spatial dimension exhibited a bigger impact on the random error correction comparing to that in the temporal dimension. This study provided strong encouragement for the efficient use of in-situ SM observations and provided valuable methods for combining multi-sources SM datasets. [ABSTRACT FROM AUTHOR]

Accurate prognostic for the remaining useful life (RUL) of lithium-ion batteries (LIBs) is extremely crucial to the stable operation and timely maintenance of a battery system. Nevertheless, battery lifespan is difficult to measure due to the capacity regeneration in non-linear and unstable degradation trend. To increase the prediction accuracy, the Time Varying Filter-based Empirical Mode Decomposition (TVF-EMD) is innovatively introduced to decompose the original capacity data into subseries. Meanwhile, the complexities of the subseries are measured by the Box-counting dimension (BCD). Moreover, Fast Ensemble Empirical Mode Decomposition (FEEMD) is exploited to further decompose the most complex subseries. Additionally, Bidirectional Gated Recurrent Unit (BiGRU) is established for (sub-)subseries prognosis. The prediction performance is further strengthened by an error correction method (ECM). Eventually, the effectiveness of the proposed prognosis framework is verified on two battery datasets. The experimental results illustrate that the maximum root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the proposed hybrid framework are merely 1.917, 0.434 and 0.706% respectively. Compared with two decomposition methods, MAE can be reduced by at least 22.73%, and a reduction of not less than 7.4% in RMSE is achieved. • A new hybrid methodis proposed for the remaining useful life forecasting. • The secondary decomposition method employed can enhance the composition ability. • The box-counting dimension is utilized to choose the most complicated sub-layers. • An error correction scheme is proposed to further enhance prediction performance. [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]

Data assimilation is a useful tool to correct the discrepancies of numerical model results by extracting reliable information from observed data. As one important category of data assimilation, updating output variable, also known as error correction, can be operated offline and give better performance with more computational efficiency. Spatial distribution procedure is one essential step for error correction since it can address the challenge for correcting the errors at locations without measurements. Currently, most attempts have been made to estimate spatial distribution pattern, or improve the competence by exploring spatial relationship, but consideration of temporal correlation or temporal lag is lacked. Fewer efforts can be found on assessing the influence of temporal lag on the spatial interpolation. This study proposes two spatial interpolation approaches, i.e. approximated space-time Ordinary Kriging (ASTOK) and temporally corrected Approximated Ordinary Kriging (TCAOK), both of which are inspired by approximated Ordinary Kriging (AOK) but take the temporal lag into consideration. These proposed schemes are compared with AOK and tested in a case study based on Singapore Regional model (SRM) and its coarse version (SRMC), to correct the water level outputs in the whole domain. The study compares the difference of the proposed approaches, and explores their adaptive capabilities by utilizing temporal lag in various manners. It also provides insight on the combination of both temporal lag and spatial lag and lays down a guide which can be utilized in other spatial interpolation procedure. The proposed scheme can also be applied to update any numerical model with insufficient observations. [ABSTRACT FROM AUTHOR]

[Display omitted] • A stepwise hybrid model is proposed for hourly solar irradiance forecasting. • The correction errors are used by a step-by-step approach. • The VMD method is proposed to reduce non-stationarity of irradiance series. • The CNN-LSTM-MLP and DBN are combined. • The feature of high and low frequencies are predicted separately. Accurate and reliable solar irradiance forecasting is critical for distribution planning and modern smart grid management and dispatch. However, due to the time series of solar irradiance with the nonlinearity and nonstationarity, some researches up to now are still unsatisfactory in terms of prediction accuracy and model generalization ability. Therefore, to improve the comprehensive performance of the model, a novel forecasting framework of hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and variational mode decomposition (VMD) was proposed. In this paper, three different datasets were pre-processed and then the step-by-step predictions of seven different methods were completed based on the proposed procedures. Finally, comprehensive analysis results of the model indicated that the prediction scheme proposed in this paper made full use of, step-by-step prediction, VMD method, integrated hybrid model, and error correction four major advantages greatly improved the anti-interference ability of the model, so that the average performance metrics RMSE, nRMSE, MAE, and R2 of the three datasets reach to 12.53 W/m2, 6.04 %, 7.65 W/m2, and 99.79 %, respectively. Moreover, the optimal promoting percentages of the R2(P R 2 )indicator compared to the persistence model (Per) is by 20.17 %. It is found that the model is superior to a large number of traditional alternative approaches in terms of accuracy and robustness, which may provide a reference for comprehensive performance optimization of the model in the future. [ABSTRACT FROM AUTHOR]

Accurately forecasting wind speed is a critical mission for the exploitation and utilization of wind power. To improve the prediction accuracy, the nonlinearity and nonstationarity embedded in wind speed time series should be reduced. Because the subseries has less nonlinearity and nonstationarity after decomposition, the decomposition-based forecasting methods are widely adopted to provide the higher predictive accuracy. However, latest studies showed the real time decomposition-based forecasting methods could be worse than the single forecasting models. The aim of this study is to improve the performance of the real time decomposition-based forecasting method after the factors attributed to its unsatisfactory performance are uncovered. In this paper, the feature selection and error correction are adopted in the real time decomposition-based forecasting method to enhance the prediction accuracy. In the proposed method, the raw wind speed time series is decomposed into a number of different subseries by ensemble empirical mode decomposition; then two feature selection methods including kernel density estimation-based Kullback-Leibler divergence and energy measure are used to reduce the disturbance of illusive components; further the least squares support vector machine is adopted to establish the one-step ahead forecasting models for the remaining subseries; finally, the hybrid of least squares support vector machine and generalized auto-regressive conditionally heteroscedastic model is introduced to correct resulting error component if its inherent correlation and heteroscedasticity cannot be neglected. Based on two sets of measured data, the results of this study show that: (1) the real time decomposition-based method may be ineffective in practice; (2) both the feature selection and error correction can improve forecasting performance in comparison with the real time decomposition-based method; (3) compared with other involved methods, the proposed hybrid method has the satisfactory performance in both accuracy and stability. [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]

In recent years, various thermal microscope imaging systems have been developed to meet the demands of micro-thermal analysis for large-scale integrated circuits, biomedical, science, and research fields. However, conventional thermal microscope imaging systems, which use cooled infrared detectors are heavy and expensive. In order to solve this problem, we developed a thermal microscope imaging system based on an uncooled infrared detector. However, the spatial resolution of the thermal microscope imaging system based on an uncooled infrared detector is low. With optical micro-scanning technology, the spatial resolution of the thermal microscope imaging system can be increased without increasing the detector dimension or reducing the detector unit size. In order to improve its spatial resolution, a micro-scanning system based on optical plate rotation was developed, and an optical microscanning thermal microscope imaging system was obtained after the integrated design. Due to environmental factors, mechanical vibration, alignment error and other factors, there is micro-scanning error in the designed micro-scanning thermal microscope imaging system. The four low-resolution images collected by micro-scanning thermal microscope imaging system are not standard down-sampled images. The quality of the image interpolated directly by four collected images is reduced and the performance of the micro-scanning system isn’t fully exploited. Therefore, based on the proposed second-order oversampling reconstruction micro-scanning error correction algorithm and the new edge directed interpolation algorithm, a new micro-scanning error correction technique is proposed. Simulations and experiments show that the proposed technique can effectively reduce optical micro-scanning error, improve the systems spatial resolution and optimize the effect of the imaging system. It can be applied to other electro-optical imaging systems to improve their spatial resolution. [ABSTRACT FROM AUTHOR]

Soft errors that corrupt the value of bits stored in registers or memories are a major issue for modern electronic systems. To ensure that they do not cause failures, error detection and correction codes are commonly used to protect memories. When memories are used for a specific application, sometimes it is possible to optimize the protection based on the knowledge of the application. One example is the memories used in network devices for packet processing. In particular, the protection of approximate membership check structures such as Bloom filters has been recently studied showing that it is possible to optimize the protection. Cuckoo filters are an alternative to Bloom filters for approximate membership check that has been recently proposed. Cuckoo filters are interesting as they are competitive in terms of memory usage for low false positive rates and also support the removal of elements. In this paper, the protection of Cuckoo filters against soft errors is studied showing that it can be enhanced by exploiting its structure and using the knowledge of the effects on an error in a Cuckoo filter. [ABSTRACT FROM AUTHOR]

The deep-sea pose measurement system can measure the relative distance and angle of two subsea pipelines which the connection of submarine pipelines project need, and the success of connection is mostly decided by high measurement accuracy. According to deep-sea environment, paper designs a measuring method by deep-sea pipeline pose measurement system which bases on the stretching wire. In order to increasing measurement accuracy, paper analyses the transitive relation of error source which impact system measurement accuracy, and gets the impact laws of error sources which include the orthogonal angle, extension arm vertical pitching angle and horizontal swing angle, etc. We revise the measuring result by binary linear regression analysis to improve the measurement accuracy. Through constructing the test platform, we compare measuring value and true value of the pipelines pose parameters. The experimental results show that the distance error is ±30 mm inside, and that the angle error is ±0.7° inside within 7 m after correction. We reduce the measurement error of the pose measurement system, and verify the correctness of the theoretical. [ABSTRACT FROM AUTHOR]

• A unified error model of the geomagnetic measurement data was established. • A two-stage Kalman filter method was proposed to perform data fusion. • An adaptive attitude updating algorithm is proposed to eliminate data interference. • The high-precision geomagnetic directional technology was achieved. Low-cost magnetic and inertial sensors based on micro-electro-mechanical systems (MEMS) are extensively used for navigation systems. However, measurements of the magnetometer are very susceptible to different factors, resulting in low accuracy when used for attitude estimation. To solve these problems, a novel method is proposed for geomagnetic directional technology based on sensor error correction and adaptive hybrid filter. In the dynamic directional accuracy test with the turntable rotating speed of 40°/s, the maximum error and RMSE of the yaw angle is 1.00° and 0.63°, respectively. When magnetic interference exists, this algorithm dynamically adjusts the weighting of the magnetometer data by information entropy and RMSE. The experimental results show that the adaptive attitude updating algorithm can reduce the directional error by 90.61% in the environment of magnetic interference. It can meet the overall demand for high-precision attitude calculation of different carriers. [ABSTRACT FROM AUTHOR]

Registers are one of the circuit elements that can be affected by soft errors. To ensure that soft errors do not affect the system functionality, Triple Modular Redundancy (TMR) is commonly used to protect registers. TMR can effectively protect against errors affecting a single flip-flop and has a low overhead in terms of circuit delay. The main drawback of TMR is that it requires more than three times the original circuit area as the flip-flops are triplicated and additional voting logic is inserted. Another alternative is to protect registers using Error Correction Codes (ECCs), but those typically require a large circuit delay overhead and are not suitable for high speed implementations. In this paper, DMR + an alternative to TMR to protect registers in FPGAs, is presented. The proposed scheme exploits the FPGA structure to achieve a reduction in the FPGA resources (LUTs and Flip-Flops) at the cost of a certain overhead in delay. DMR + can correct all single bit errors like TMR but is more vulnerable to multiple bit errors. To evaluate the benefits, the DMR + technique has been implemented and compared with TMR considering standalone registers and also some simple designs. [ABSTRACT FROM AUTHOR]

Aiming at precisely predicting the life of the high-power white light LED (HPWLED), a three-parameter Weibull function and the right approximation method were employed to establish the luminance degradation model. The lumen maintenance data collected according to the IES LM-80-08 lumen maintenance test standard were fitted with and without error corrections, and the pseudo failure time of each HPWLED sample was extrapolated. The statistical analysis on the failure time was achieved by using Weibull distribution, normal distribution, lognormal distribution and Akaike Information Criterion (AIC). Then the life information was acquired. The results indicate that Weibull right approximation luminance degradation model (WRALDM) accurately reflects the variation of the lumen law with time. The failure time is accurately obtained. The best life distributions before and after the error correction to the lumen maintenance data are identified, based on AIC, as Weibull distribution and lognormal distribution, respectively. It is further confirmed by comparing the widths of life confidence interval and the life provided by the IES TM-21-11 method that the HPWLED life using WRALDM has a better accuracy. The optimized model provides researchers and manufacturers with significant guidelines for the further development of life prediction methodology. [ABSTRACT FROM AUTHOR]

Currently, the demand for reliable and high performance computing is increasing due to the enlarging susceptibility of computing circuits to different environmental effects, and the advent of diverse computation-based applications. Arithmetic operators, as the main building blocks of the processing units in computing systems, are exposed to different types of single or multiple errors incurred by different faults which can seriously damage the whole system. In this paper, a new approach is presented to achieve fault-tolerant carry look-ahead adder architectures, much more efficient than the conventional methods, with the characteristic of robustness against multiple simultaneous errors. The proposed method is based on revising the carry generation block to achieve multiple error correction capability, and utilizing a modified parity prediction-based method that in combination with the proposed error correction scheme leads to multiple error detection capability. Verified by simulations, analytical assessments show that, as well as correcting or detecting all single permanent or transient errors, multiple simultaneous errors in the new carry look-ahead adders are corrected or at least detected with a high probability independent of the number of errors. Apart from having more reliability against multiple errors, these adders require lower area overheads compared to the state of the art designs as well as conventional fault-tolerant methods. [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 current study investigated the human ability to synchronize movements with event sequences containing continuous tempo changes. This capacity is evident, for example, in ensemble musicians who maintain precise interpersonal coordination while modulating the performance tempo for expressive purposes. Here we tested an ADaptation and Anticipation Model (ADAM) that was developed to account for such behavior by combining error correction processes (adaptation) with a predictive temporal extrapolation process (anticipation). While previous computational models of synchronization incorporate error correction, they do not account for prediction during tempo-changing behavior. The fit between behavioral data and computer simulations based on four versions of ADAM was assessed. These versions included a model with adaptation only, one in which adaptation and anticipation act in combination (error correction is applied on the basis of predicted tempo changes), and two models in which adaptation and anticipation were linked in a joint module that corrects for predicted discrepancies between the outcomes of adaptive and anticipatory processes. The behavioral experiment required participants to tap their finger in time with three auditory pacing sequences containing tempo changes that differed in the rate of change and the number of turning points. Behavioral results indicated that sensorimotor synchronization accuracy and precision, while generally high, decreased with increases in the rate of tempo change and number of turning points. Simulations and model-based parameter estimates showed that adaptation mechanisms alone could not fully explain the observed precision of sensorimotor synchronization. Including anticipation in the model increased the precision of simulated sensorimotor synchronization and improved the fit of model to behavioral data, especially when adaptation and anticipation mechanisms were linked via a joint module based on the notion of joint internal models. Overall results suggest that adaptation and anticipation mechanisms both play an important role during sensorimotor synchronization with tempo-changing sequences. This article is part of a Special Issue entitled SI: Prediction and Attention . [ABSTRACT FROM AUTHOR]

• A novel signal decomposition algorithm based on FPA-VMD is proposed. • BiLSTM neural network is employed to capture the deep temporal features. • Multiple meteorological factors are considered to improve the model's performance. • The proposed model can perform better for multi-step ahead wind power forecasting. To reduce the effect of nonlinearity and volatility in the wind power time sequence, a two-stage short-term wind power forecasting method based on optimized decomposition prediction and error correction is proposed. In the first stage, in order to improve the decomposition effect of variational mode decomposition (VMD), the decomposition loss is defined as the evaluation criterion to guide the parameter setting of VMD, and flower pollination algorithm (FPA) is utilized to automatically optimize the parameters of VMD. Then the complex wind power sequence is decomposed into simple intrinsic mode functions (IMFs). Besides, bi-directional long short-term memory (BiLSTM) neural network is built for each IMF to explore the deep time-series features of wind power in both past and future directions. In the second stage, to reduce the correlation among meteorological factors, principal component analysis (PCA) is employed to convert the multi-dimensional meteorological factors into low-dimensional principal components. Then, with the input of IMFs and principal component, an error correction model based on BiLSTM neural network is established to reduce the inherent error of the model. The experimental results show that the proposed method has higher prediction accuracy than the traditional methods in single-step and multi-step ahead forecasting. [ABSTRACT FROM AUTHOR]

Radiation-induced multiple bit upsets (MBUs) degrade the reliability of scaled static random access memory (SRAM)-based field programmable gate arrays (FPGAs). Reducing the correction time for MBU and preventing the error accumulation are the challenges faced by error correction code (ECC) integrated FPGAs. In this paper, a novel built-in ECC using encode-and-compare of the data and parity bits is proposed to reduce the correction time and improve the reliability of FPGA. Implementation has been carried out in FPGA to confirm its effectiveness. The proposed method is 5 times faster than existing CRC based inbuilt error mitigation solution. This work opens a door for 2-D ECC to be universally used in FPGAs for safety-critical applications. [ABSTRACT FROM AUTHOR]

Two experienced English as a Foreign Language (EFL) teachers transformed a traditional pencil-and-paper writing classroom that focused on sentence writing mechanics for Taiwanese graduate students by incorporating in-class text chat to augment students’ regular classroom regime. This blended-style classroom afforded the teachers enhanced monitoring and guidance of students during the writing process. Students practiced sentence writing, offered peer support during learning, and interacted in real-time practice of sentence patterns taught. The incorporation of text-chat into the traditional classroom increased the amount of interaction amongst students as well as between students and teachers. Findings revealed that students often supplemented their in-class text chat on-line writing with spoken discourse but seldom utilized other on-line technologies available. This action research investigating technological innovation in a writing course facilitated the identification of emerging issues within EFL academic writing classes mediated by in-class chat-rooms, namely: peer support, self-correction and punctuation. [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]

Error correction and peer feedback have been recognized as vital in second language writing development. This study developed a web-based error correction practice mechanism which was attached to an online annotation system for EFL writing instruction. In this system, students input new essays with the Essay Editor and the teacher marked students' errors with the Annotation Editor. Students could read the corrected essay and the results of error analysis through the Viewer. Based on the results of error analysis through the Error Correction Practice Recommender, the system recommended an essay to the student to practice error correction and to implement peer feedback. Following the peer feedback exercise, the student could compare his/her corrections with the teacher's corrections on the same essay through the Viewer. A pretest–posttest study was also conducted to evaluate the effects of using the system on 35 EFL students' writing performance and peer feedback performance. The experiment consisted of four rounds of writing and peer feedback practice. The results of students' writing error ratios and error correction ratios showed that the system was effective in improving students' written accuracy and error correction performance in the peer feedback process. A range of recommendations for future research are discussed. [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]

Highlights: [•] We analyze the market share of each public transport mode in total PT ridership. [•] Cointegration and error correction techniques are applied. [•] Short and long run effects of various factors are computed. [•] Fare, GDP and total PT ridership are the main determinants of PT mode shares. [•] Adjustment towards long run equilibrium takes approximately two months. [ABSTRACT FROM AUTHOR]

Abstract: The grey theory can be applied in the research of prediction, decision-making and control, especially in prediction. However, traditional grey models show some limitations which affect to the model applicability and the prediction accuracy. So, this paper proposes a novel comprehensive adaptive grey model CAGM(1,N) in order to overcome the disadvantages existing in the traditional GM(1,N). The proposed model is developed from GM(1,N) model with three improvements. The first one is a use of two smartly additive factors to convert any raw data into a grey sequence which satisfies both the raw data checking condition and quasi-smooth condition to perform the grey estimation. The second one is a modification in calculating the background series which affect to the grey model accuracy. And the final improvement is a modification in computing the model output by using error correcting accumulation to eliminate the residual prediction errors. The CAGM(1,N) model can be applied to any practical prediction problem and obtain higher fitting and prediction accuracy comparing with the traditional GM(1,N) model. The case study has been carried out to certify the improved model. [Copyright &y& Elsevier]

Cyclic redundancy checks (CRC) are widely used in transmission protocols to detect whether errors have altered a transmitted packet. It has been demonstrated in the literature that CRC can also be used to correct transmission errors. In this paper, we propose an improvement of the state-of-the-art CRC-based error correction method. The proposed approach is designed to significantly increase the error correction capabilities of the previous method, by handling a greater part of error cases through the management of candidate lists and using additional validations. Simulations and results for wireless video communications over 802.11p and Bluetooth Low Energy illustrate the Peak Signal-to-Noise Ratio (PSNR) and visual quality gains achieved with the proposed approach versus the state-of-the-art and traditional approaches. These gains range on average from 1.6 dB to 7.3 dB over Bluetooth Low Energy channels with Eb/No ratio of 10 dB and 8 dB, respectively. • The addition of validation steps can enhance the CRC-based error correction. • Adding checksum validation greatly reduces the candidate's list size. • The parity of the CRC syndrome can avoid unnecessary computations. • The CRC-based correction can improve the quality of wireless video transmissions. • The error distribution has a significant impact on the proposed method's gains. [ABSTRACT FROM AUTHOR]

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]

• Error types affect how frequently learners correct errors in L2 essays using a corpus. • Error types affect how accurately learners correct errors in L2 essays using a corpus. • Corpus use helps learners' easy access to the L2 target phrases. • Corpus use helps learners' easy access to frequency information of co-occurring words. • Corpus use especially helps learners correct omission errors in their L2 essays. This study reports classroom research on corpus use in second-language (L2) error correction to explore how information from corpus data influences L2 writing. As the error types for which corpus consultation is helpful remain uncertain, this study examined the error types for which data-driven learning (DDL) provides good error correction in L2 writing to maximize the benefits of corpus use in L2 class instruction. First, 55 undergraduate students from a university in Tokyo wrote an essay in 25 min without access to reference resources and received teacher or peer feedback on their errors. They then performed revision tasks for 15 min with either use or non-use of reference resources (corpus or dictionary). This procedure was carried out 9–11 times during the term. Error analysis found that error types affected how frequently and accurately learners corrected their errors using corpus data. Corpus use allowed easy access to the exact target phrases and frequency information of co-occurring words, which especially helped participants correct omission errors, and corpus consultation to correct omission errors increased over time. Teachers should consider error types so that DDL can promote accurate error correction in L2 writing and serve as a practical option in L2 classrooms. [ABSTRACT FROM AUTHOR]

This study investigated whether students' article accuracy could be improved through a digital game which provides a realistic business task in which a player serves as an employer interviewing potential employees and the English article system is employed for communication during conversational turn taking. Research participants were divided into three groups: the digital game-based learning group, the cloze exercise with corrections group, and the cloze exercise group. Three job application letter writing tasks served as pretests, immediate posttests and delayed posttests. The results of the immediate posttests revealed that the cloze exercise with corrections group outperformed the other two groups in terms of English article accuracy (F = 11.915, p =.000). No significant difference was discovered between the digital game-based learning and cloze exercise groups. In the 1-month-delayed posttest, the cloze exercise with corrections group still outperformed the cloze exercise group in terms of English article accuracy (F = 18.460, p =.000). However, the digital game-based learning group, beyond all expectations, significantly outperformed the cloze exercise with corrections group. The digital game that provides focused correction on article errors may be more beneficial to learners than traditional teacher correction. [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]

This work presents a new decoding algorithm that extends the error-correction capacity of group testing based (GTB) non-binary error correction codes without modifying the number of parity symbols of the codeword. A list decoding algorithm based on the pattern of the syndromes takes advantage of the parity matrix equation exploiting its structure. The decoding algorithm can be reformulated by detecting superimposed errors (errors that affect to the same parity check equation). The list of superimposed patterns applies the extrinsic information of the non-superimposed locations to correct the errors. The list of non-superimposed locations simply apply a majority-logic pattern. The complexity of the proposed solution is lower, as it does not require complex operations such as multiplications or inversions in the Galois Field and shares the magnitude equations for both cases, keeping the rest of the equations with the same number of comparisons as the original single-symbol error correction algorithm. • New error-correction GTB algorithm with larger correction capacity without increasing parity symbols • Efficient algorithm to correct double-symbol errors • Increasing the coding gain with low complexity for decoding [ABSTRACT FROM AUTHOR]

• Reinforcement learning can efficiently decode uncorrelated quantum errors in toric codes. • Code threshold is very close to the 11% theoretical bound. • Policy free deep Q-learning is sufficiently versatile to address correlated noise. • Free deep Q-learning for uncorrelated noise yields essentially minimum weight matching pattern. We apply deep reinforcement learning techniques to design high threshold decoders for the toric code under uncorrelated noise. By rewarding the agent only if the decoding procedure preserves the logical states of the toric code, and using deep convolutional networks for the training phase of the agent, we observe near-optimal performance for uncorrelated noise around the theoretically optimal threshold of 11%. We observe that, by and large, the agent implements a policy similar to that of minimum weight perfect matchings even though no bias towards any policy is given a priori. [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]

• Propose a novel short-term wind speed prediction model base on deep learning. • Employ the double decomposition method to process wind speed data. • Modify the forecasting errors in the error correction strategy. • Verify the validation of the proposed model by four real cases. As wind power accounts for an increasing proportion of the electricity market, the wind speed prediction plays a vital role in the stable operation of the power grid. However, owing to the stochastic nature of wind speed, predicting wind speeds accurately is difficult. Aims at this challenge, a new short-term wind speed prediction model based on double decomposition, error correction strategy and deep learning algorithm is proposed. The complete ensemble empirical mode decomposition with adaptive noise and variational mode decomposition are applied to decompose the original wind speed series and error series, respectively. The deep learning algorithm based on long short term memory neural network, is utilized to detect the long-term and short-term memory characteristics and build the suitable prediction model for each sub-series. In the four real forecasting cases, nine models were built to compare the performance of the proposed model. The experimental results show that the proposed model performs better than all other considered models without double decomposition, and the variational mode decomposition for error series can improve the effect of error correction strategy. [ABSTRACT FROM AUTHOR]