Real-time Correction Method Research Articles

Real-time correction of light dilution effect for ship emission monitoring of SO2

With the rapid development of the shipping industry, ship emissions have become a focal point in environmental protection. SO2, as a major component of ship emissions, is crucial to monitor to ensure environmental compliance. SO2-sensitive ultraviolet (UV) cameras represent an advanced emerging technology for remote sensing monitoring of ship emissions. However, as monitoring distance increases, errors in the monitoring results due to the light dilution (LD) effect rise significantly. The aim of this study is to address the LD effect in SO2 monitoring for mobile pollution sources and propose a real-time correction method. Based on the atmospheric radiative transfer model and developed data processing algorithms, the method corrects the LD effect in real-time, enhancing the accuracy and reliability of SO2 UV camera monitoring. Experimental data collected from ship emissions at Yantai port are used to validate the accuracy of the correction method. Results show that the LD effect can lead to a 60% underestimation in the monitoring results at a distance of 4 km. The proposed method effectively corrects the LD effect, improves the accuracy of the monitoring results, lays the foundation for the engineering application of UV cameras in ship exhaust monitoring, and therefore promotes the wide application of UV cameras in air quality monitoring and environmental protection.

Online Prediction and Correction of Static Voltage Stability Index Based on Extreme Gradient Boosting Algorithm

With the increasing integration of renewable energy sources into the power grid and the continuous expansion of grid infrastructure, real-time preventive control becomes crucial. This article proposes a real-time prediction and correction method based on the extreme gradient boosting (XGBoost) algorithm. The XGBoost algorithm is utilized to evaluate the real-time stability of grid static voltage, with the voltage stability L-index as the prediction target. A correction model is established with the objective of minimizing correction costs while considering the operational constraints of the grid. When the L-index exceeds the warning value, the XGBoost algorithm can obtain the importance of each feature of the system and calculate the sensitivity approximation of highly important characteristics. The model corrects these characteristics to maintain the system’s operation within a reasonably secure range. The methodology is demonstrated using the IEEE-14 and IEEE-118 systems. The results show that the XGBoost algorithm has higher prediction accuracy and computational efficiency in assessing the static voltage stability of the power grid. It is also shown that the proposed approach has the potential to greatly improve the operational dependability of the power grid.

Real-time correction of gain nonlinearity in electrostatic actuation for whole-angle micro-shell resonator gyroscope

MEMS gyroscopes are well known for their outstanding advantages in Cost Size Weight and Power (CSWaP), which have inspired great research attention in recent years. A higher signal-to-noise ratio (SNR) for MEMS gyroscopes operating at larger vibrating amplitudes provides improved measuring resolution and ARW performance. However, the increment of amplitude causes strong nonlinear effects of MEMS gyroscopes due to their micron size, which has negative influences on the performance. This paper carries out detailed research on a general nonlinear mechanism on the sensors using parallel-plate capacitive transducers, which is called the gain nonlinearity in electrostatic actuation. The theoretical model established in this paper demonstrates the actuation gain nonlinearity causes the control-force coupling and brings extra angle-dependent bias with the 4th component for the whole-angle gyroscopes, which are verified by the experiments carried out on a micro-shell resonator gyroscope (MSRG). Furthermore, a real-time correction method is proposed to restore a linear response of the electrostatic actuation, which is realized by the gain modification with an online parameter estimation based on the harmonic-component relationship of capacitive detection. This real-time correction method could reduce the 4th component of the angle-dependent bias by over 95% from 0.003°/s to less than 0.0001°/s even under different temperatures. After the correction of actuation gain nonlinearity, the bias instability (BI) of whole-angle MSRG is improved by about 3.5 times from 0.101°/h to 0.029°/h and the scale factor nonlinearity (SFN) is reduced by almost one order of magnitude from 2.02 ppm to 0.21 ppm.

Spatial frequency domain imaging combining profile correction enables accurate real-time quantitative mapping of optical properties of apples

Spatial frequency domain imaging technology enables non-contact, wide-field quantitative analysis of optical properties in strong turbid media, thereby enhancing bruise detection in fruits based on changes in optical property values. However, precise and real-time measurement of the optical properties of curved biological materials is challenging due to the significant influence of tissue contours. This paper primarily presents a research study on real-time correction method based on the property that DC component diffuse reflectance (Rd_DC) and AC component diffuse reflectance (Rd_AC) are influenced by the contour trend of the tissue in a similar manner. The aim of this method is to eliminate the significant impact of sample contour angle on demodulation and enhance the detection of apple bruise. Sinusoidal illumination at a spatial frequency of 0.2 mm−1 was projected at 730 nm, with the experiments carried out on standard reflector, polytetrafluoroethylene sphere, and non-bruised and bruised apples. The results showed that, the relative error of the planar standard reflector decreased from the maximum value of 52.85–4.74%. The median of the Rd_AC for the polytetrafluoroethylene spherical standard, which fluctuated between 0.6 and 0.8, was corrected to the theoretical value of 1. For normal apples, the maximum fluctuation in the standard deviation of Rd_AC, after median filtering, reduced from 172.94% at 300×300 pixels to 18.69%. The boxplot of Rd_AC for bruised apples became smaller, with the lower fence being corrected from nearly 0–0.1750, which closely matched the theoretical value of 0.17. Additionally, the correlation of reduced scattering coefficient (μs′) in the ideal region of interest (100×100 pixels) remained at 0.9785 before and after correction, while other areas were corrected, making the bruising more pronounced. The correction method did not require additional experiments, with the consumed time in millisecond level.

A deep-learning real-time bias correction method for significant wave height forecasts in the Western North Pacific

Significant wave height (SWH) is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the European Centre for Medium-Range Weather Forecasting System Integrated Forecast System Global Model (ECMWF-IFS). This method was built on the trajectory gated recurrent unit deep neural network, and it conducts real-time rolling correction for the 0–240-h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972–46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794–38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.

A Real-Time Energy Response Correction Method for Cs3Cu2I5:Tl Scintillating Dosimeter.

The uneven energy response of radiation detectors severely limits the accuracy of the dose rate meter used for radiation protection. Currently widely used in dose rate meters as a physical method of setting shielding compensation, the energy response correction error of the detector at different energies is mostly between 15 and 25%. This work designs a real-time correction method for energy response based on a novel Cs3Cu2I5:Tl scintillation detector to improve the accuracy of the dose rate meter used for radiation protection. The technique utilizes the idea of pulse amplitude weighting (PAW) to segment the pulse amplitude histogram. This detector achieves an almost constant energy response after our correction. The experimental results show that compared to 137Cs γ rays, the maximum error of the response is 8.26% in the photon energy ranging from 33 keV to 1.25 MeV, which is much better than ±30% of the recommended IEC 61526:2010, verifying the feasibility of PAW.

Real-time fetal brain tracking for functional fetal MRI.

To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.

Temperature correction of post mortem quantitative magnetic resonance imaging using real-time forehead temperature acquisitions

Performing magnetic resonance imaging (MRI) of deceased is challenging due to altered body temperatures compared to in vivo temperatures and, hence, requires a temperature correction. This study investigates the possibility to correct brain MRI parameters real-time and non invasively based on the forehead temperature.17 post mortem cases were included and their forehead temperatures were measured continuously during the in situ brain MRI protocol consisting of a diffusion tensor imaging, multi-contrast spin echo, multi-echo gradient echo and inversion recovery spin echo sequence. Linear models were fitted to the quantitative MRI parameters in a forensically interesting temperature range for white matter, cerebral cortex and deep gray matter, separately, and the influence of the forehead temperature on the MRI parameters was determined.A statistically significant temperature sensitivity was found for T2 and mean diffusivity in white matter, for T1 in cerebral cortex, as well as for T1 and mean diffusivity in deep gray matter.Linear models were computed to temperature correct these MRI parameters in in situ post mortem scans to allow their comparison regardless of temperature.The here presented real-time and non invasive temperature correction method for the brain presents a crucial precondition for quantitative in situ post mortem MRI.

A Rolling Real-Time Correction Method for Minute Precipitation Forecast Based on Weather Radars

The quantitative precipitation estimation by weather radar plays an important role in observations and forecasts of meteorological processes. The National Minute Quantitative Precipitation Forecast system of China (MQPF), providing location-based refined short-term and imminent precipitation forecasting services, filled the gap in the official minute precipitation service products in China’s meteorological field. However, due to the technical limitations of radar itself and the complexity of the atmosphere, the corresponding relationship between radar echoes and surface precipitation is unstable. Based on radar and precipitation data from meteorological stations, a rolling real-time correction method is proposed to improve precipitation prediction accuracy through rolling correction of spatial and temporal structural errors in MQPF products. The results show the following: (1) Although this method may lead to a certain increase in the missing ratio, the significant improvement in the false alarm ratio after rolling correction has a positive guiding effect on short-term public meteorological services. (2) Regarding the time to complete rolling correction, the longest and shortest times appear in April and December, respectively. The mean running time to achieve correction of spatial and temporal error corrections ranges from 3.8 s to 6.4 s and 7.7 s to 11.5 s, respectively, which fully meets the real-time operational requirements of radar business.

Scatter correction for cone-beam CT via scatter kernel superposition-inspired convolutional neural network

Objective. X-ray scatter leads to signal bias and degrades the image quality in Computed Tomography imaging. Conventional real-time scatter estimation and correction methods include the scatter kernel superposition (SKS) methods, which approximate x-ray scatter field as a convolution of the scatter sources and scatter propagation kernels to reflect the spatial spreading of scatter x-ray photons. SKS methods are fast to implement but generally suffer from low accuracy due to the difficulties in determining the scatter kernels. Approach. To address such a problem, this work describes a new scatter estimation and correction method by combining the concept of SKS methods and convolutional neural network. Unlike conventional SKS methods which estimate the scatter amplitude and the scatter kernel based on the value of an individual pixel, the proposed method generates the scatter amplitude maps and the scatter width maps from projection images through a neural network, from which the final estimated scatter field is calculated based on a convolution process. Main Results. By incorporating physics in the network design, the proposed method requires fewer trainable parameters compared with another deep learning-based method (Deep Scatter Estimation). Both numerical simulations and physical experiments demonstrate that the proposed SKS-inspired convolutional neural network outperforms the conventional SKS method and other deep learning-based methods in both qualitative and quantitative aspects. Significance. The proposed method can effectively correct the scatter-related artifacts with a SKS-inspired convolutional neural network design.

Rapid distortion correction enables accurate magnetic resonance imaging-guided real-time adaptive radiotherapy.

Magnetic resonance imaging (MRI)-Linac systems combine simultaneous MRI with radiation delivery, allowing treatments to be guided by anatomically detailed, real-time images. However, MRI can be degraded by geometric distortions that cause uncertainty between imaged and actual anatomy. In this work, we develop and integrate a real-time distortion correction method that enables accurate real-time adaptive radiotherapy. The method was based on the pre-treatment calculation of distortion and the rapid correction of intrafraction images. A motion phantom was set up in an MRI-Linac at isocentre (P0 ), the edge (P 1) and just outside (P 2) the imaging volume. The target was irradiated and tracked during real-time adaptive radiotherapy with and without the distortion correction. The geometric tracking error and latency were derived from the measurements of the beam and target positions in the EPID images. Without distortion correction, the mean geometric tracking error was 1.3mm at P 1 and 3.1mm at P 2. When distortion correction was applied, the error was reduced to 1.0mm at P 1 and 1.1mm at P 2. The corrected error was similar to an error of 0.9mm at P0 where the target was unaffected by distortion indicating that this method has accurately accounted for distortion during tracking. The latency was 319±12ms without distortion correction and 335±34ms with distortion correction. We have demonstrated a real-time distortion correction method that maintains accurate radiation delivery to the target, even at treatment locations with large distortion.

A deep learning method for real-time bias correction of wind field forecasts in the Western North Pacific

Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases. The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved “double-encoder forecaster” architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8–11% and 9–14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Accurate and real-time network calculation for mine ventilation without wind resistance measurement

A real-time correction method is proposed to accurately and comprehensively collect mine air volume data using wind velocity sensors; ventilation network calculation is realized without relying upon wind resistance measurements. For single-roadway wind velocity correction, an improved Kalman filter is employed to identify the error and adapt to dynamic changes in the ventilation system, improving the error updating process and thus the convergence rate. For node air mass flow balance correction, three types of sensor redundancy layouts are summarized with corresponding correction methods. The principle of the proposed real-time network calculation algorithm is discussed, the calculation method for each sensor layout is provided, and the robustness is analyzed accordingly. Simulation experiment results demonstrate significant improvements in the accuracy of the data obtained using the proposed data correction and network calculation algorithm. Thus, the designed real-time calculation algorithm can be practically applied to realize the accurate collection of ventilation data.

On-Board Thermal Motion Compensation Method for Pointing Errors of the Remote Sensor Aboard a Three-Axis Stabilized Geostationary Satellite

For earth observation from a three-axis stabilized geostationary (GEO) remote-sensing satellite, a highly accurate onboard thermal motion compensation (TMC) method for real-time correction of the satellite sensor’s line-of-sight (LOS) pointing error due to thermally induced structural distortion internal to the sensor during the diurnal cycle remains a global concern to date. In this letter, we propose a novel TMC method for GEO sensors. Compared with the traditional TMC methods, this method has the following two advantages. First, we define the LOS misalignment angle to model the comprehensive impact of the sensor’s internal thermal misalignment angles on the sensor’s LOS pointing behavior, which makes the optical path modeling simpler and more generic. Second, we fit the repeatable diurnal variations of the LOS misalignment angle based on long time-series star observations; therefore, the calculation and the use of the TMC amount are no longer limited to the assumption that the sensor’s internal thermal misalignment angles remain constant within a certain period of time, as in the traditional method. The proposed TMC method is theoretically applicable to all types of GEO sensors, including optical and microwave sensors, and is verified by Fengyun-4A advanced geosynchronous radiation imager (FY-4A/AGRI) TMC experiments.

Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on the EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

Stochastic Modeling for Estimating Real-Time Inundation Depths at Roadside IoT Sensors Using the ANN-Derived Model

This paper aims to develop a stochastic model (SM_EID_IOT) for estimating the inundation depths and associated 95% confidence intervals at the specific locations of the roadside water-level gauges, i.e., Internet of Things (IoT) sensors under the observed water levels/rainfalls and the precipitation forecasts given. The proposed SM_EID_IOT model is an ANN-derived one, a modified artificial neural network model (i.e., the ANN_GA-SA_MTF) in which the associated ANN weights are calibrated via a modified genetic algorithm with a variety of transfer functions considered. To enhance the reliability and accuracy of the proposed SM_EID_IOT model in the estimations of the inundation depths at the IoT sensors, a great number of the rainfall induced flood events as the training and validation datasets are simulated by the 2D hydraulic dynamic (SOBEK) model with the simulated rain fields via the stochastic generation model for the short-term gridded rainstorms. According to the results of model demonstration, Nankon catchment, located in northern Taiwan, the proposed SM_EID_IOT model can estimate the inundation depths at the various lead times with high reliability in capturing the validation datasets. Moreover, through the integrated real-time error correction method integrated with the proposed SM_EID_IOT model, the resulting corrected inundation-depth estimates exhibit a good agreement with the validated ones in time under an acceptable bias.

Phase retrieval for studying the structure of vitreous floaters simulated in a model eye

We simulate vitreous floaters in a model eye and apply a phase retrieval algorithm to recover the structure of the vitreous floaters. The algorithm requires modulating the object under study by a relatively small number of masks. The modulating masks are generated by a spatial light modulator. The retrieved phase of the vitreous floaters is compared to that measured with a surface profiler. It can be concluded that the phase of the vitreous floaters can be retrieved in presence of speckles, however, the high computational load is one of the most significant drawbacks of the algorithm. In order to apply the proposed method for real-time correction of the vitreous floaters, the speed of the algorithm must be increased and second-order tracking of the vitreous floaters must be ensured.

An improved real-time cycle slip correction algorithm based on Doppler-aided signals for BDS triple-frequency measurements

Doppler, which is an instantaneous GNSS observable signal, has been proven effective in determining velocity and acceleration due to its high availability and accuracy. We propose a real-time triple-frequency cycle slip correction (CSC) method based on Doppler-aided signals because Doppler shift is time-independent and immune to cycle slips. When the sampling interval is less than 1 s, cycle slips on triple-frequency can be detected and repaired using pure Doppler data with high reliability; however, this method cannot be used when the sampling interval exceeds 1 s because the integral cumulative error of Doppler increases significantly. For such cases, a modified triple-frequency CSC approach has been developed based on the raw phase and smoothed code data that was refined using the Doppler signal. To suppress the effect of the integral Doppler error, a balance factor is introduced to adjust the contributions of the raw code and Doppler observables. After the refinement of the GNSS data, three independent combinations need be selected to detect and repair cycle slips with triple-frequency observations. Four constrained criteria have been proposed to select optimal combinations that can reduce the residual ionospheric delay (RID) and measurement noise to a low level. Finally, experiments were carried out to test the performance of the new method using real triple-frequency BDS observations (GPST: 3:15:00–5:55:00, March 23, 2018). The results show that pure Doppler can detect and repair cycle slips effectively with small intervals, and modified Hatch-Melbourne-Wübbena (HMW) method based on Doppler-aided signals can achieve 99.7% success rate in cycle slip correction with large intervals (up to 30 s).

Motion correction methods for MRS: experts' consensus recommendations.

Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B0 field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B0 homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B0 field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B0 field correction requires internal navigator methods to measure the B0 field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.

利用GNSS差分的天线阵大气相位扰动实时修正方法

利用GNSS差分的天线阵大气相位扰动实时修正方法