Error Correction Method Research Articles

Study on error correction method of English long sentence translation based on support vector machine

Study on error correction method of English long sentence translation based on support vector machine

Study on error correction method of English long sentence translation based on support vector machine

Study on error correction method of English long sentence translation based on support vector machine

Token-Level Fact Correction in Abstractive Summarization

This paper addresses fact correction for abstractive summarization of which aim is to edit a system-generated summary into a new source-consistent summary. The summaries generated by abstractive summarization models often contain various kinds of factual errors. Thus, fact correction becomes essential to apply abstractive summarization to real-world applications. However, most existing methods for fact correction focus only on entity-level errors, which occasions the error correction methods to miss non-entity errors such as inconsistent tokens or mentions. Therefore, this paper presents a token-level fact correction that resolves inconsistencies of a system-generated summary at the token level. Since a token is the smallest meaning-bearing unit, all kinds of errors can be corrected if the errors are rectified at this level. The proposed fact corrector examines the consistency of a summary at the summary level like existing methods, but corrects the found inconsistencies at the token level. Thus, the proposed corrector consists of three modules of a summary fact checker, a token fact checker, and a fact emender. The summary fact checker inspects if a system-generated summary is factually consistent with a source text, the token fact checker finds out the tokens which cause inconsistency, and the fact emender actually replaces the inconsistency-causing tokens with correct tokens in the source text. Since these modules are closely related and affect one another, they are jointly trained to improve the performance of each module. The effectiveness of the proposed fact corrector is empirically proven from two viewpoints of consistency and summarization performance. For correcting inconsistencies in a summary, it is shown that the summaries by the proposed corrector are more factually consistent than those by its competitors. In addition, the proposed corrector outperforms the current state-of-the-art corrector even in automatic summarization performance.

Triangular Series Fitting-Based Harmonic Error Correction Method for Indirect Time-of-Flight Camera

Triangular Series Fitting-Based Harmonic Error Correction Method for Indirect Time-of-Flight Camera

Low-Frequency Attitude Error Compensation for the Jilin-1 Satellite Based on Star Observation

Owing to changes in the space thermal environment, the optical axis angle between the camera and star tracker (Cam-ST) changes as well, resulting in regular low-frequency attitude errors and a decline in the geometric positioning accuracy of the images. In this study, a star-observation-based low-frequency attitude error correction method is proposed. First, the star was used as the control point to be observed by satellite from different latitudes in an orbit. The star information is extracted using a priori attitude to obtain the camera pointing in inertial coordinates. Second, combined with the optical axis pointing value of the star tracker, the angle change between the camera and two star trackers (Cam-2STs) was calculated. A polynomial model was used to fit the angle change, and the fitting model and camera optical axis pointing compensation algorithm were used to compensate for the camera optical axis. Third, the regular low-frequency attitude error of the camera was realized. The experiment used 20 groups of star observation data from two missions of the Jilin-1 07 video satellite. After compensation, the camera pointing maximum root mean square error was reduced from 30.08" to 7.70", the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> -axis attitude error was reduced from 13.26" to 7.09", and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i> -axis from 29.48" to 7.36". The geometric positioning accuracy of Jilin-1 ground images was 25.63 m after correction. The experimental results showed that the proposed method effectively improved the attitude measurement accuracy. This method can thus effectively improve the direct geometric positioning accuracy of a single image.

Guidance error correction method based on target track

Guidance error correction method based on target track

Error of Measuring Surface Temperature of Turbine Blades With the Influence of Reflection by Monochromatic Method

A new method is proposed for correcting the error of monochromatic method in measuring the surface temperature of turbine blades with the influence of reflection. The measurement error caused by surrounding components were numerically investigated. The effects of background temperature, adjacent rotor blades, front stator vanes and rear stator vanes on the temperature measurement error are described in detail. Furthermore, the corrected errors of high emissivity and low emissivity are compared and analyzed and the influence of the relative displacement between the turbine blade and the hot-end component on the temperature measurement error is also discussed. Benefiting from the proposed radiation-based error correction method (RECM), we can reduce the monochromatic temperature measurement error to less than 0.3 % at high emissivity and less than 0.8% at low emissivity theoretically. Finally, the RECM method is studied experimentally, and the results show that the temperature measurement error can be corrected to better than 0.6%, which is in good agreement with the Monte Carlo simulation results.

Nexuses between Economic Growth and Health Indicators: Evidence from Pakistan

The goal of this article is to look at how various health indices affect Pakistan's economic growth. To achieve this goal, error correction and co-integration methods were used by using time series data from 1990 to 2022. The goal of this research is to examine the short-run and long-term temporal connections between health and per capita GDP growth. In the long-run, there is a significant relationship between per capita GDP and economic health indicators, which are significantly affecting per-capita GDP. According to the results of short run, health indicators have no meaningful influence on per-capita GDP. While indicators of health have a considerable long-run influence in economic growth. It implies that the influence of health indicators can only impact economic growth in the long run. The study's main result suggests financial gain, through growing and raising the stock of healthy human capital, particularly if present stocks are at a low level. Improved health has a two-way interaction with the economic process. This research is to examine the short-run and long-term temporal connections between health and per capita GDP growth, using mistreatment Co-integration and Error Correction. Long-term health and economic process studies would be extremely valuable in determining the achievable magnitudes of the entire cumulative effects of health on economic process. Two key hypotheses would be examined; the first would be that 'health influences economic growth' might be a long-standing temporal development. Second, what role do health output and input factors play in per capita GDP growth?

Examining the Nexus between External Debt and Economic Growth in Bangladesh: An ARDL-ECM Approach

This paper aims to examine the nexus between external debt and economic growth in Bangladesh for periods ranging from 1973 to 2019. Time-series econometric techniques of Autoregressive Distributed Lag Model (ARDL) and Error Correction Method (ECM) are utilized in empirical analysis to investigate whether short-run and long-run dynamics exist between external debt and the growth of Bangladesh. Findings derived from the analysis reveal a long-run association between external debt and Bangladesh's economic growth. The error correction method also points out long-run adjustment if any disequilibrium arises in a period in the short run. The study recommends maintaining a stable position of overseas debt and ensuring compatible investment conditions for domestic and foreign investors. IUBAT Review—A Multidisciplinary Academic Journal, 5 (1): 26-34

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

PENGARUH BONUS DEMOGRAFI, PENGANGGURAN DAN INFLASI TERHADAP PERTUMBUHAN EKONOMI DI PROVINSI JAMBI

This study examined the effect of demographic bonuses, unemployment, and inflation on economic growth in Jambi Province. This study used secondary data from the Central Bureau of Statistics of Jambi Province during 2000-2021. The data were analyzed using the Vector Error Correction Method (VECM). All tests used in this study were Stationarity Test, Lag Length Criteria Test, VAR Stability Test, Cointegration Test, Granger Causality Test, and VECM Estimation. The results showed that in the short term, the demographic bonus (TPAK) and unemployment had a negative and insignificant effect, while inflation had a positive and insignificant effect on economic growth in Jambi Province. In the long term, Demographic Bonus had a positive and significant effect on economic growth in Jambi Province, while unemployment had a negative and insignificant effect, and inflation had a negative and significant effect on economic growth in Jambi Province.

Sector Level Analysis of FDI-Growth Nexus: A Case Study of Pakistan.

The main objective of the study is to empirically investigate the differential impact of services and manufacturing Foreign Direct Investment (FDI) on economic growth over the period of 1972 to 2008. The study further examines the role of FDI in presence of macroeconomic instability and privatisation. For the investigation of long run, Autoregressive distributed lag model (ARDL) has been used. For short run results, we used Error correction method (ECM). Our empirical results show that FDI inflow in both, service sector as well as manufacturing is contributing to economic growth positively. But it is apparent from the results that contribution of services FDI to growth is greater than that of manufacturing FDI to growth. Furthermore, the results provide coherent and sound policy recommendations for further policy adaptation regarding sectors. JEL classification: F23, F36, F43, C32 Keywords: Foreign Direct Investment, Economic Growth, Manufacturing Sector, Service Sector, Co-integration

Error correction for Mueller matrix ellipsometry based on a reference optical path.

To reduce the error induced by light source fluctuations, motor rotation instability, environment, and other factors in real-time measurements using dual-rotating compensator Mueller matrix ellipsometry (DRC-MME), an error-correction method based on a reference optical path is proposed. A modified Mueller matrix expression was derived theoretically by introducing the reference optical path of the DRC-MME system. Simulation analysis and experimental verification of the air, polarizer, and S i O 2 samples were performed using this method. The absolute error of the Mueller matrix elements of the S i O 2 samples decreased from less than 0.028 to less than 0.008, and the standard deviation decreased from 0.9% to 0.3%. This method has certain guiding significance in improving the measurement accuracy of DRC-MME.

Objectivity verification experiment of the dynamic system response curve method for streamflow simulation

Error correction methods are widely used to improve the performance of streamflow modeling. The Dynamic system response curve (DSRC) is a newly developed error correction method that utilizes error feedback correction to obtain updated variables and thus enhances model accuracy. While the DSRC method is simple and practical, its objectivity has not been systematically demonstrated. To address this issue, the paper proposes an experiment to verify its objectivity. First, three hydrological models (Xinanjiang (XAJ), Hydrologiska Byråns Vattenavdelning (HBV) and vertically hybrid yielding (VHY)) were calibrated and three corrected areal mean rainfall series were obtained by using the DSRC-based models (XAJ-DSRC, HBV-DSRC and VHY-DSRC). The corrected rainfall series from one hydrological model was then used as input to other hydrological models, and its performance was compared to those of using the original rainfall series by five performance measures (Nash-Sutcliffe efficiency coefficient (NSE), root mean squared error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and correlation coefficient (R)). Finally, this objectivity verification experiment was applied to different model combinations for further demonstration. Case studies of the Sunshui River and Shiquan catchments in China showed that when the DSRC-corrected rainfall series from either hydrological model was applied to the original or other hydrological models, an increase in NSE and R values and a decrease in RMSE and MAE values were observed. The absolute values of PBIAS were within 10%. Accordingly, the hypothesis that the DSRC method is objective is not rejected based on the results of the experiment.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.

Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China. Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods. From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447). The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Consistent inverse probability of treatment weighted estimation of the average treatment effect with mismeasured time-dependent confounders.

In longitudinal studies, the inverse probability of treatment weighted (IPTW) method is commonly employed to estimate the effect of time-dependent treatments on an outcome of interest. However, it has been documented that when the confounders are subject to measurement error, the naive IPTW method which simply ignores measurement error leads to biased treatment effect estimation. In the existing literature, there is a lack of measurement error correction methods that fully remove measurement error effect and produce consistent treatment effect estimation. In this article, we develop a novel consistent IPTW estimation procedure for longitudinal studies. The key step of the proposed method is to use the observed data to construct a corrected function that is unbiased of the unknown IPTW function. Simulation studies reveal that the proposed method outperforms the existing consistent and approximate measurement error correction methods for IPTW estimation of the average treatment effect. Finally, we apply the proposed method to analyze a real dataset.

Correcting Underestimation and Overestimation in PolInSAR Forest Canopy Height Estimation Using Microwave Penetration Depth

PolInSAR is an active remote sensing technique that is widely used for forest canopy height estimation, with the random volume over ground (RVoG) model being the most classic and effective forest canopy height inversion approach. However, penetration of microwave energy into the forest often leads to a downward shift of the canopy phase center, which leads to model underestimation of the forest canopy height. In addition, in the case of sparse and low forests, the canopy height is overestimated, owing to the large ground-to-volume amplitude ratio in the RVoG model and severe temporal decorrelation effects. To solve this problem, in this study, we conducted an experiment on forest canopy height estimation with the RVoG model using L-band multi-baseline fully polarized PolInSAR data obtained from the Lope and Pongara test areas of the AfriSAR project. We also propose various RVoG model error correction methods based on penetration depth by analyzing the model’s causes of underestimation and overestimation. The results show that: (1) In tall forest areas, there is a general underestimation of canopy height, and the value of this underestimation correlates strongly with the penetration depth, whereas in low forest areas, there is an overestimation of canopy height owing to severe temporal decorrelation; in this instance, overestimation can also be corrected by the penetration depth. (2) Based on the reference height RH100, we used training sample iterations to determine the correction thresholds to correct low canopy overestimation and tall canopy underestimation; by applying these thresholds, the inversion error of the RVoG model can be improved to some extent. The corrected R2 increased from 0.775 to 0.856, and the RMSE decreased from 7.748 m to 6.240 m in the Lope test area. (3) The results obtained using the infinite-depth volume condition p-value as the correction threshold were significantly better than the correction results for the reference height, with the corrected R2 value increasing from 0.775 to 0.914 and the RMSE decreasing from 7.748 m to 4.796 m. (4) Because p-values require a true height input, we extended the application scale of the method by predicting p-values as correction thresholds via machine learning methods and polarized interference features; accordingly, the corrected R2 increased from 0.775 to 0.845, and the RMSE decreased from 7.748 m to 6.422 m. The same pattern was obtained for the Pongara test area. Overall, the findings of this study strongly suggest that it is effective and feasible to use penetration depth to correct for RVoG model errors.

An Error Correction Method for PD Apparent Charge Measurement in Power Transformers

When partial discharge (PD) measurement is performed on power transformers under the IEC60270 standard, a large error will occur in apparent charge estimation due to the winding distributive longitudinal capacitances and unknown location of the PD source. A frequency-spectrum-based PD localization and an apparent charge error correction method are proposed based on winding RLC ladder network gray-box modeling with FRA results and intelligent algorithm. An experiment on a 220 kV inhomogeneous model winding is carried out and the proposed method is applied. The results show that the maximum error of apparent charge has reduced from 37.7 dB to less than 3 dB which shows an error decrease of 70%, verifying the feasibility of this method on real windings. The proposed method requires only single-end PD measurement and follows strictly the IEC60270 standard, which is hopefully to be extended to more complex transformer windings in future research.

Tertiary Education and Economic Growth of Nepal: An ARDL-ECM Analysis

This study examined the relationship between tertiary schooling and economic growth in Nepal. The autoregressive distributed-lagged (ARDL) bound test for cointegration and error-correction method (ECM) were employed. Time series data of 1989/90 to 2018/19 were taken into consideration. Updated time series and ARDL bounds test were applied for cointegration with ECM analysis to estimate the aimed relationship between variable of interests. Economic growth (RGDP) as dependent variable, education (GERT) as a regressor, and controlled variables––physical capital, labor force, and CPI––were used. The results of the study revealed cointegration between education and economic growth, confirming their long-run relationship. The negative and significant ECT1-t strengthened the presence of long-run relationship among RGDP and GERT. This study found tertiary schooling as a productivity enhancer of human capital in Nepal. Based on this finding, this study recommends promoting the tertiary schooling through adequate financing.

A new empirical method for correction of daytime air temperature measurement errors in naturally ventilated radiation shields

A new empirical method for correction of daytime air temperature measurement errors in naturally ventilated radiation shields