Correction Method Research Articles

Comparison and Analysis of Three Methods for Dynamic Height Error Correction in GNSS-IR Sea Level Retrievals

Comparison and Analysis of Three Methods for Dynamic Height Error Correction in GNSS-IR Sea Level Retrievals

Toward the reliable use of reanalysis data as a reference for bias correction in climate models: A multivariate perspective

General circulation models are vital tools in climate change research, profoundly influencing numerous hydroclimatological studies, but often exhibit systematic biases due to limitations in parameterization. To correct these biases, bias correction (BC) methods, which are heavily dependent on the quality of historical reference data, are employed. Ideally, accurate data comes from a dense network of gauges, but due to their limited availability, reanalysis data is frequently used instead. Although reanalysis data provides consistent global and temporal coverage, its assimilated nature can introduce additional biases, potentially increasing error propagation particularly in the multivariate perspective. Thus, this study investigates the reliability of reanalysis data as a reference for multivariate bias correction (MBC) and utilizes a two-stage bias correction approach that integrates both gauge and reanalysis data to leverage their respective strengths. Conducting a case study in South Korea, we utilized gauge data, ERA5 reanalysis data, and outputs from 10 CMIP6 General Circulation Models. We implemented three-nested experiments to test the robustness of this two-stage approach across various dimensions. Results show that using reanalysis data as a reference can lead to bias propagation, especially misrepresenting precipitation and wind dependencies in high elevation areas. However, the two-stage approach has shown an enhancement in mitigating these limitations, even when gauge data is temporally sparse, and across different combinations of MBC methods. Finally, we highlight the current limitations and potential for future research to focus on effectively combining gauge and reanalysis data to compensate for each other’s weaknesses, thereby enhancing the accuracy and reliability in future climate realizations.

Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis

During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals. Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.

Assessing clustering methods using Shannon's entropy

Unsupervised clustering techniques are crucial for effectively partitioning datasets into meaningful subgroups. In this paper, we introduce a novel clustering fuzziness metric based on Shannon's entropy, which quantifies the level of uncertainty in clustering assignments. This metric is employed to develop a statistical test for evaluating clustering algorithms and to propose parametric corrections to enhance their performance. We provide empirical evidence showing that our approach significantly improves clustering quality compared to well-known algorithms such as Fuzzy K-Means (FKM) and Fanny. For instance, applying our correction method led to a notable decrease in Jensen-Shannon Divergence (JSD) values, indicating enhanced clustering accuracy. Our methodology is demonstrated through comprehensive experiments on both simulated and real-world datasets. Additionally, our approach proves effective in determining the optimal number of clusters, showing superiority over traditional methods. These results highlight the practical benefits of our metric and correction techniques for improving clustering performance.

Characterization of a real time dosimetry system using 2D nano and micro-coatings in proton and carbon therapeutic ion beams

Real-time radioluminescence two-dimensional coatings have potential as dosemeters in proton and carbon therapeutic beams. We investigated coatings made of nano and micro-(C44H38P2)MnCl4 and (C38H36P2)MnBr4 crystals mixed with a water-equivalent substrate. The response of the radioluminescence signal of the coatings along the Bragg curves presented an ionization quenching effect, but less prominent than what has been observed in our previous works using Al2O3:X (X = C and C,Mg) coatings. We hypothesize that this results from their lower crystal sizes and effective atomic number (Zeff). Combined experimental results and Monte Carlo simulations resulted in correction factors to address the linear energy transfer dependence and restore the constant response for particle therapy beams. The quenching correction method was applied to the studied proton and carbon ion beams and yielded the best results for the nano-(C44H38P2)MnCl4, coating, followed by the micro-(C44H38P2)MnCl4, nano-(C38H36P2)MnBr4, and micro-(C38H36P2)MnBr4.

Technical note: A validated correction method to quantify organic and inorganic carbon in soils using Rock-Eval® thermal analysis

Technical note: A validated correction method to quantify organic and inorganic carbon in soils using Rock-Eval® thermal analysis

Using Iterative Correction to Improve the Accuracy of the Blind-Hole Welding Residual Stress Test.

An iterative correction method for the stress release coefficient, leveraging numerical simulation, has been innovatively developed to address the significant error issues associated with the blind-hole method in high-stress residual stress testing of steel structures. This method effectively reduces measurement error in high residual stress regions through a discriminant iteration process. The finite element analysis technology was employed to accurately simulate the blind-hole method's test process, and additionally, Python was utilized for customizing the secondary development of ABAQUS software, thereby automating and optimizing the method. When compared with simulation and experimental data from the welding process, the efficiency, accuracy, and reliability of the correction method have been verified. The proposed method eliminates the need for tedious calibration experiments inherent in traditional methods, significantly enhancing the test's automation level and convergence speed, and ensuring measurement accuracy, which provides an innovative solution for the accurate evaluation of residual stress in steel structures.

Bias correction of daily precipitation from climate models, using the Q‐GAM method

AbstractClimate models are useful tools for analyzing historical and projecting future climate conditions. However, the model results tend to differ systematically from observations, particularly for parameters with complex spatial and temporal distributions such as precipitation. A combination of quantile mapping and generalized additive models (GAMs) is presented and proposed as a new method (Q‐GAM) for the bias correction of daily precipitation. Q‐GAM is demonstrated by using data from five European stations with different climate characteristics. For each station, the closest continental grid point of a EURO‐CORDEX climate model was selected for bias correction. A bootstrapping experiment is presented with over 1000 repetitions of randomly splitting the historical period 1981 to 2005 into a calibration and evaluation period. The results for all stations reveal that Q‐GAM is a straightforward, accurate and computationally efficient method for the bias correction of precipitation. In particular, the method improves the frequency of dry days and the total annual rainfall amount. This outcome is robust across stations with varying climate characteristics and also to the choice of calibration and evaluation periods. Similar results are also obtained for other precipitation characteristics such as the 0.9 and 0.95 quantiles.

Quantitative impact damage of apple based on hyperspectral imaging combined with mechanical parameters and size correction.

In order to solve the problem of decreasing the accuracy of quantitative prediction of damage of fruits resulting in the size difference of fruits, the spectral correction method based on the size difference of fruits was adopted. To provide richer theoretical knowledge for the quality detection of fruits and the design of damage reduction programs in reality. First, the undamaged spectra of the group of apples with better performance of the model were selected as the reference spectra by analyzing and comparing the modeling results of the prediction models of mechanical parameters with the single fruit diameter groups. The spectral correction coefficient was calculated with the formulas, and the damage spectra of three groups of apples were size-corrected by this coefficient to build the mechanical parameter models. Finally, the corrected spectra were screened for characteristic wavelengths by competitive adaptive reweighting and uninformative variable elimination algorithms. The results of study showed that the correlation coefficients of the prediction set of the models were improved by 2.1%-13% and the root mean square errors were reduced by 16%-51% with the spectrally corrected models compared with the precorrection models. Therefore, the size correction method can be used to eliminate the effect of size difference on the mechanical parameter models to improve the applicability of the quantitative damage prediction models, and it can provide the theoretical guidance to design the loss-reducing protective measures and the agricultural mechanized operation process.

Continuity of long-term climate data series after the transition from manual to automatic weather station

In climatology, transitioning from conventional manual weather stations (MWS) to automatic weather stations (AWS) presents challenges in maintaining the homogeneity of long-term data series. At the Skalnaté Pleso Observatory (1778 m a.s.l., High Tatras), manual meteorological measurements have been conducted since 1943 using the same methods and devices at the same location. In 2014, the AWS Physicus was installed to record meteorological data parallelly with manual measurements. This study processed six years of parallel measurements (2017–2022) to derive corrections for AWS monthly atmospheric precipitation totals and maximum (Tmax), minimum (Tmin) and mean air temperatures (Tmean) measured in 2023. Two correction approaches were proposed: monthly regressions (MR) and cumulative distribution functions (CDF), including Generalized Extreme Value (GEV) distribution, normal distribution (GAUSS), and Gamma distribution (GAMMA). Analyses of monthly data revealed an underestimation of precipitation totals in AWS data, with a mean bias error (MBE) of −6.8 mm and a root mean squared error (RMSE) of 20.0 mm. The most suitable correction method was the MR, which decreased MBE to 1.5 mm and RMSE to 11.1 mm. AWS monthly air temperature data were overestimated by 0.1 °C, 0.3 °C, and 0.1 °C for Tmax, Tmin, and Tmean, respectively. For Tmax, correction using the MR and GEV methods reduced the MBE to 0.0 °C and achieved RMSE of 0.1 °C for both. After applying GAUSS, MBE and RMSE decreased to 0.1 °C. The most appropriate correction method for Tmin was the MR resulting in MBE of 0.0 °C and RMSE of 0.3 °C. For Tmean, the MR and GEV methods reduced the MBE to 0.0 °C and the RMSE to 0.1 °C for both methods. These results demonstrate that AWS monthly data, when corrected using the presented methods, have the potential to maintain the continuity of historical climate data series at Skalnaté Pleso.

Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?

Urban green spaces (UGS) provide ecological and habitat benefits such as carbon sequestration, oxygen production, humidity increase, noise reduction, and pollution absorption. UGS maps derived from remote sensing images serve as the fundamental data for urban planning and carbon sequestration assessments. However, the spatial resolution of remote sensing image and the pattern of urban structures significantly influence UGS mapping, making it challenging to obtain accurate UGS maps. To investigate the impact of spatial resolution on UGS mapping, this study utilized five different spatial resolution datasets: Gaofen2 (1 m, 4 m), Sentinel2 (10 m), and Landsat8 (15 m, 30 m). Random forest, LightGBM, and support vector machine were employed to map UGS, and the accuracies of UGS maps at different spatial resolutions were compared. Subsequently, the spatial distribution patterns of uncertainties in UGS maps were analyzed from both overall and urban functional zone perspectives. Furthermore, the uncertainty analysis of UGS mapping was conducted considering different landscape patterns in urban functional zones. The results indicate: (1) UGS map varies at different spatial resolution. Higher uncertainties associated with coarser spatial resolutions. Medium and coarse spatial resolution images inadequately capture the fine-grained distribution of urban green spaces. (2) Uncertainty in UGS mapping at different spatial resolutions is generally consistent in spatial distribution. From a functional zoning perspective, the accuracy of green space mapping over non-natural zones is sensitive to spatial resolution. (3) The distribution pattern of UGS patches affects the accuracy of UGS mapping. Uncertainty can be reduced in UGS mapping at medium and coarse spatial resolutions based on UGS landscape pattern indices by multiple linear regression, random forest and LightGBM model. This study comprehensively reveals that uncertainties in mapping UGS from multi-spatial resolution remote sensing images vary across urban functional zones and landscape pattern indices, and it is the first attempt to propose methods for UGS area correction based on landscape pattern indices. The results of this study will facilitate the application of remote sensing data at different spatial resolutions in urban areas.

Prognostic criteria for surgical treatment of stress urinary incontinence in combination with cystocele in postmenopausal women

Background. The relevance of the problem of stress urinary incontinence (SUI) in combination with cystocele has increased significantly over the past decades, due to the high prevalence of the disease in the population, as well as increased awareness of patients about treatment options. It is known that one-stage surgical treatment significantly reduces the need for re-intervention and, accordingly, the rehabilitation period. The identification of significant criteria that are decisive in the effectiveness of various methods of surgical correction of SUI in combination with cystocele dictates the need for further research in this direction.The purpose of the study. To determine outcome criteria for surgical treatment of stress urinary incontinence in combination with cystocele in postmenopausal women based on a comprehensive preoperative examination.Materials and methods. A prospective randomized study was conducted, which included 80 women with stress urinary incontinence in combination with postmenopausal cystocele, who were routinely hospitalized in the gynecological department of the Federal State Budgetary Institution Research Institute of OMM for surgical treatment. All patients, after signing voluntary informed consent to participate in the study and conducting a comprehensive preoperative examination using a random number generator, were randomized into two groups of equal size. The first group included 40 women who received surgical treatment including anterior colporrhaphy in combination with urethropexy with a synthetic loop; the second group consisted of 40 patients who underwent surgical treatment using the method of paraurethral injection of a volume-forming gel in combination with anterior colporrhaphy.Results and discussion. Criteria for predicting the outcome of surgical treatment of stress urinary incontinence in combination with cystocele in postmenopausal women were determined based on a comprehensive preoperative examination. Conclusion: Analysis of complex urodynamic and ultrasound examination methods in women revealed statistically significant differences between groups of women with positive and negative outcomes of surgical treatment. The identified criteria for predicting the outcome of surgical treatment serve to assist the clinician in choosing the optimal, most effective method of treating SUI in combination with cystocele in postmenopausal women.Conclusions. Analysis of complex urodynamic and ultrasound methods of examination in women revealed statistically significant differences between groups of women with positive and negative outcomes of surgical treatment. The identified criteria for predicting the out-come of surgical treatment serve to assist the clinician in choosing the optimal, most effective method of treating SUI in combination with cystocele in postmenopausal women.

A Software Tool for the True Height Analysis of Ionograms Using the Iterative Gradient Correction (IGC) Method

AbstractDeriving the precise true height electron density profile from the measured ionosonde virtual heights is quite a challenging problem. Recently, Ankita and Tulasi Ram (2023, https://doi.org/10.1029/2023RS007808) presented a new method, Iterative Gradient Correction (IGC) method, for true height analysis that uses HF radio wave propagation path computations to reconstruct the true height profile. Through iterative corrections on electron density gradients between successive points, the IGC method minimizes errors below a specified tolerance at each point and reconstructs a complete electron density profile. The derived profiles from the IGC method are found to be accurate when compared with Incoherent Scatter Radar and Global Navigation Satellite System—Radio Occultation observations. To facilitate true height analysis by IGC method for a wider user community, a MATLAB‐based software has been developed and is outlined in this report. The software can be installed on any Windows platform and is designed with a user‐friendly interface for easy and efficient application by the users. It can analyze multiple scaled ionograms in a single run and outputs the real height profiles in ASCII format. Further, the software also captures important ionospheric parameters such as the base altitudes and peak frequencies of E‐ and F‐layers (e.g., hE, hF, foE, and foF2) etc., from the computed true height profiles and tabulates in a separate output file for the ready use. The software also provides the option for extrapolation of true height profile into top‐side ionosphere up to a user‐specified height and reconstructs the complete vertical electron density profile.

Long-term functional results of reconstructive plastic surgery on the pelvic floor

Background. With all the variety of treatment methods for pelvic organ prolapse (POP), the criteria for cure are reduced to the absence of anatomical defects of the pelvic floor. At the same time, the complete restoration of the physical, psycho-emotional and social components of a woman’s life is practically not taken into account. Studying the quality of life, as well as the sexual function of patients with POP will help to clarify the effectiveness of the synergy of surgical treatment and hardware rehabilitation methods.The purpose of the study is to assess the quality of life and sexual function of women of reproductive age after reconstructive plastic surgery on the pelvic floor in combination with radiofrequency exposure.Material and methods. An open prospective longitudinal study was conducted on 60 patients of reproductive age with stage II-III genital prolapse according to the POP-Q classification. The women were divided into two groups: group 1 — patients who underwent vaginal plastic surgery with their own tissues (n=30); group 2 — patients who, after reconstructive plastic interventions, underwent postoperative rehabilitation using dynamic quadripolar radiofrequency (n=30). Before surgery, 1 month and 1 year after treatment, a survey was conducted to determine the quality of life using the Short Form-36 questionnaire and sexual function using the Female Sexual Function Index and Female Sexual Distress Scale.Research results. The combination of surgical correction of pelvic organ prolapse with subsequent radio wave therapy makes it possible to correct anatomical defects, and at the same time statistically significantly increases the index of quality of life and female sexual function, reducing a woman’s experiences associated with problems in her sexual life.Conclusion. The synergy of surgical methods of correction and postoperative rehabilitation with the use of DCRF contributes to the long-term preservation of the functional results of treatment of pelvic floor incompetence during reproductive age.

Clinical and immunological efficacy of complex therapy for bronchial asthma in children with the inclusion of components of far eastern brown algae

Classical basic therapy for asthma is aimed at stopping inflammation, primarily atopic, and has achieved significant positive results in the treatment of the disease, which has led to a decrease in the severity of asthma. The proportion of patients in whom asthma control is achieved does not exceed 30%; complete control is achieved in 5%, that is associated with the influence non-atopic factors on the pathogenesis of BA, leading to the search for additional methods of correction. Despite the identified biological, immunomodulatory properties of the components of brown algae, the mechanisms of their action, in asthma, are not sufficiently illuminated, which became the purpose of this study: to determine the possible immunomodulatory effect of a functional food product in children with asthma based on brown algae (Far Eastern kelp) and its use in addition to standard therapy to improve control of the course of this disease. Children with asthma (n = 56) aged from 5 to 17 years were comprehensively examined and divided into 2 groups: those who received a functional food product in addition to standard therapy (n = 20) and those who received only standard therapy (n = 36). Laboratory tests included CBC, immunogram, cytokines (IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL- 10, IL-18, TNFá), blood ICC MPM, and herpesvirus DNA. Against the background of complex treatment, changes in the interleukin profile (IL-6, IL-10, IL-18) were noted, characteristic of a decrease in the intensity of inflammatory processes, including nonspecific ones, expressed in a tendency towards a decrease in the total number of lymphocytes, an increase in the immunoregulatory index both due to an increase in the number of T helper cells and and reduction of killer T cells. This was confirmed by a decrease in monocytes and ESR. Improved energy supply of immunocompetent blood cells (granulocytes and monocytes), a decrease in the number of patients with active replication of EBV and herpes virus type 6 indicate its immunomodulatory effect. The result of these changes was the positive clinical dynamics of asthma control indicators, which allows us to recommend the inclusion of a functional food product based on Far Eastern brown algae in addition to standard therapy for children with asthma aged 5-17 years.

Improving SMF coupling efficiency in aberrated optical system through optical system aberration correction method based on MPLC

In spatial laser communication terminals, the distortion of wavefront caused by the aberrations due to optical system processing and installation errors reduces the coupling efficiency of single-mode fiber (SMF). A method based on Multi-Plane Light Conversion (MPLC) is proposed to correct optical system aberrations and improve the coupling efficiency by converting the distorted light into Gaussian beam which matches SMF through a succession of phase planes. The Gaussian beam to SMF coupling efficiency model is firstly established and demonstrates that the SMF coupling efficiency can be effectively improved to 100% by converting the incident light into Gaussian beam which matches the SMF mode. The optical system aberration correction comparison simulation and experiment is conducted, and the results show after correcting aberrations using MPLC, the coupling efficiency increases from less than 0.1%–58.61% compared to distorted light directly coupling into SMF. Results show that the optical system aberrations are effectively corrected by this method, which has a certain reference value for the study of improving the coupling efficiency.

Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau

Water resources in the soil play an essential role in hydrological processes and ecosystem functions on the Tibetan Plateau. However, accurately measuring soil moisture distribution in this region presents challenges due to the diverse ecosystem types, complex terrain, and harsh environmental conditions. In this study, we introduce an approach for estimating mesoscale soil moisture in the Qilian Mountains (QLM) region of the eastern Tibetan Plateau using a cosmic-ray neutron rover. Soil moisture estimates derived from neutron count rates, newly adjusted by vegetation effects, demonstrated good agreement with soil moisture measurements obtained through soil sampling at 26 calibration sites across the region (RMSE = 0.025 g g−1). The calibration parameter N0_NDVI was 443 cpm in the QLM. Utilizing NDVI as vegetation correction method showed potential improvements in the accuracy of converting neutron counts to soil moisture across the diverse mountainous ecosystem types. The newly developed calibration equation provided a high-precision, high spatial resolution soil moisture transect across various landscapes measured by the rover. The average mesoscale soil moisture along the rover route varied by ecosystem types, with values of 0.10 g/g in deserts, 0.17 g/g in grasslands, 0.13 g/g in forests, 0.18 g/g in subalpine shrublands, and 0.20 g/g in croplands. Land cover types emerged as crucial determinants of mesoscale soil moisture variability in the QLM region. These findings offer valuable mesoscale soil moisture data and new insights into soil water information at the transect scale across diverse ecosystem types in the Tibetan Plateau.

Real-time assessment of ship collision risk using image processing techniques

The poor quality or the miss of Automatic Identification System (AIS) data may cause erroneous judgement of the potential navigational risk. Therefore, this study proposes a real-time framework for assessing ship collision risk using onboard video data in order to improve the risk perception ability of navigators. Firstly, the Squeeze-and-Excitation (SE) attention mechanism and the K-means algorithm are simultaneously utilized for the framework to enhance the multi-scale ship detection capability. The Deep-SORT is employed to complete multi-ship feature matching. Secondly, the distances between two ships and their speeds are measured using the pinhole imaging principle based on the ship visual feature extraction results. Moreover, the ship distance-speed correction method is designed to improve the reliability of estimated results. Finally, the effectiveness of the framework is validated using naturalistic driving data from the “He Hua Hai” ship. The results show that the proposed framework could demonstrate an excellent performance in assessing ship collision risk using the onboard video data. The proposed framework could help precisely detect and promptly provide warnings about potential ship collision risks. This could help prevent catastrophic accidents that pose a threat to oceans and coasts, particularly in situations when AIS data proves to be unreliable or ineffective.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Functional partially linear single index models with measurement error in both functional and real covariates

In this paper, we investigate the functional partially linear single index models with contaminated functional and real covariates when the functional sample grid is dense. To eliminate the influence of measurement error on the estimations of the proposed models, a two-stage estimation procedure is introduced. In the first stage, we utilize a kernel presmoothing regression to denoise the corrupted functional curves. In the second stage, the fitted functional curves are applied to replace the original ones to obtain the predictor of unknown parameter in parametric component with the attenuation correction method and then we derive the predictor of unknown link function in nonparametric part with kernel method. Meanwhile, in order to improve computational efficiency, functional slice inverse regression method is employed to estimate the functional single index parameter. The asymptotic properties are also stated in this process at the situation of independent and identical distribution sample under some mild conditions. A simulation study and real data analysis are further explored to illustrate the good performance of our methodology.