Dynamic Correction Research Articles

Weekly dynamic motor insurance ratemaking with a telematics signals bonus-malus score

Abstract We present a dynamic pay-how-you-drive pricing scheme for motor insurance using telematics signals. More specifically, our approach allows the insurer to apply penalties to a baseline premium on the occurrence of events such as hard acceleration or braking. In addition, we incorporate a bonus-malus system (BMS) adapted for telematics data, providing a credibility component based on past telematics signals to the claim frequency predictions. We purposefully consider a weekly setting for our ratemaking approach to benefit from the signal’s high-frequency rate and to encourage safe driving via dynamic premium corrections. Moreover, we provide a detailed structure that allows our model to benefit from historical records and detailed telematics data collected weekly through an onboard device. We showcase our results numerically in a case study using data from an insurance company.

An ADCP Attitude Dynamic Errors Correction Method Based on Angular Velocity Tensor and Radius Vector Estimation

An acoustic Doppler current profiler (ADCP) installed on a platform produces rotational tangential velocity as a result of variations in the platform’s attitude, with both the tangential velocity and radial orientation varying between each pulse’s transmission and reception by the transducer. These factors introduce errors into the measurements of vessel velocity and flow velocity. In this study, we address the errors induced by dynamic factors related to variations in attitude and propose an ADCP attitude dynamic error correction method based on angular velocity tensor and radius vector estimation. This method utilizes a low-sampling-rate inclinometer and compass data and estimates the angular velocity tensor based on a physical model of vessel motion combined with nonlinear least-squares estimation. The angular velocity tensor is then used to estimate the transducers’ radius vectors. Finally, the radius vectors are employed to correct the instantaneous tangential velocity within the measured velocities of the vessel and flow. To verify the effectiveness of the proposed method, field tests were conducted in a water pool. The results demonstrate that the proposed method surpasses the attitude static correction approach. In comparison with the ASC method, the average relative error in vessel velocity during free-swaying movement decreased by 20.94%, while the relative standard deviation of the error was reduced by 17.38%.

A Dynamic Early Warning Model for Flash Floods Based on Rainfall Pattern Identification

AbstractFlash floods are one of the most devastating natural hazards in mountainous and hilly areas. In this study, a dynamic warning model was proposed to improve the warning accuracy by addressing the problem of ignoring the randomness and uncertainty of rainfall patterns in flash flood warning. A dynamic identification method for rainfall patterns was proposed based on the similarity theory and characteristic rainfall patterns database. The characteristic rainfall patterns were constructed by k-means clustering of historical rainfall data. Subsequently, the dynamic flood early warning model was proposed based on the real-time correction of rainfall patterns and flooding simulation by the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) model. To verify the proposed model, three small watersheds in China were selected as case studies. The results show that the rainfall patterns identified by the proposed approach exhibit a high correlation with the observed rainfall. With the increase of measured rainfall information, the dynamic correction of the identified rainfall patterns results in corresponding flood forecasts with Nash-Sutcliffe efficiency (NSE) exceeding 0.8 at t = 4, t = 5, and t = 6, thereby improving the accuracy of flash flood warnings. Simultaneously, the proposed model extends the forecast lead time with high accuracy. For rainfall with a duration of six hours in the Xinxian watershed and eight hours in the Tengzhou watershed, the proposed model issues early warnings two hours and three hours before the end of the rainfall, respectively, with a warning accuracy of more than 0.90. The proposed model can provide technical support for flash flood management in mountainous and hilly watersheds.

Analysis of the radiative corrections and dynamic extensions to the local field in the effective refractive index of particle suspensions

We analyze the predictions of two recently developed effective-medium approximations for the effective refractive index of a system of either transparent or plasmonic spherical particles, dispersed randomly in a transparent liquid matrix, as a function of their geometrical and physical parameters. The importance and significance of these approximations is that besides the radiative corrections to the optical response of the particles, they include full dynamic corrections to the field exciting any given particle. We perform this analysis by comparing the values obtained using them with the ones obtained from three well-known and widely used effective-medium approximations: Maxwell Garnett, Maxwell Garnett-Mie and van de Hulst. We provide plots of the real and imaginary parts of the effective index of refraction, for the five approximations considered, for polystyrene and gold nanoparticles of different sizes, as a function of the filling fraction and wavelength, pointing out the relevance of the new predictions, as well as the actual physical processes behind the so-called dependent scattering and what we now call dependent absorption.

A comparative analysis of the sensitivity and BOLD contamination of the VASO response at 3 Tesla: ME-DEPICTING vs. ME-EPI readouts

Abstract ‘Non-BOLD fMRI’ data acquired at non-zero echo time (TE) suffer from contamination by the Blood Oxygenation Level Dependent (BOLD) signal due to the unavoidable signal decay caused by transverse relaxation. This contamination further reduces their already low inherent functional sensitivities and makes their correction essential. The Slice-Saturation Slab-Inversion Vascular Space Occupancy (SS-SI–VASO), for instance, cancels out BOLD contributions from VASO data, reflecting cerebral blood volume (CBV) changes, via a dynamic division approach. Alternatively, multi-echo (ME) data provide the possibility of extrapolating to TE=0. Acquisitions at very short TE would minimize the need for such corrections. The center-out EPI variant (‘DEPICTING’) is one such readout which allows for short TE. The ME 2D DEPICTING was compared here against a traditional ME 2D EPI for its sensitivity to functional changes in the VASO signal. The two BOLD-correction schemes were also evaluated. Clear differences in functional sensitivity were observed for the uncorrected VASO data obtained from the first echo, TE1, of the two readouts. VASO data corrected by ME extrapolation were, however, found to be almost identical in their sensitivity for detecting CBV changes for both readouts. An excessively high increase in VASO signal sensitivity observed with the dynamic division correction for both readouts revealed a near-perfect linear dependence on TE of VASO signal changes. This could be attributed to the substantial intravascular BOLD contributions at 3 T. In the present data, extravascular ΔR2* fraction was found to be around ~50–60%. ME extrapolation is, hence, recommended to avoid overestimation of functional CBV changes at commonly used TEs.

Development of a control algorithm for a boost converter with the possibility of dynamic correction of control system parameters

RELEVANCE of the study lies in the development of an algorithm for controlling a DC voltage converter capable of providing a high-quality transient process with a wide change in the input parameters of the control object, affecting its nonlinear properties. purpose. THE PURPOSE. To consider methods for the development of continuous and discrete control systems for a step-up DC voltage converter and to obtain an analytical dependence of accounting for the nonlinear properties of the converter that affect the quality of regulation of the output voltage of the voltage stabilizer METHODS. The method of the average geometric root was chosen as a method for calculating the coefficients of the DC voltage converter control system, and the current circuit is adjusted using the technical optimum method, and the voltage circuit using the symmetric optimum method. The obtained analytical solutions of mathematical models and simulation models were compared in the SimInTech software environment. results. RESULTS. The simulation results show that the analytical solution of the mathematical model of the voltage stabilization system has a shorter transition time than the simulation results. This is due to the fact that in the mathematical model according to which the continuous control algorithm was synthesized, the presence of a power input filter, as well as the degree of their precharge, is not taken into account. Because of this, the transition time on simulation models is slower, applied 4 times than in the mathematical model. conclusion. CONCLUSION. When comparing the algorithms with each other, it can be seen that the algorithm obtained by converting a continuous algorithm by the Tusten method has the shortest transition time than all other algorithms. The continuous algorithm and the algorithm obtained by the inverse Euler transformation method have a speed close to each other, and the algorithm obtained by the direct Euler transformation turns out to be the slowest.

The influence of static versus dynamic pressure distribution strategies for modelling nonlinear waves generated by ships

A moving static pressure distribution is commonly used to simulate a travelling ship. However, the ship movement changes the fluid velocity around the hull, inducing pressures on the hull surface that are no longer equal to the static pressure. Therefore, we introduce a dynamic pressure correction strategy, which can accurately simulate the impact of the ship movement on the hull-surface pressure and preserve the desired hull shape under both stationary and transient conditions. The strategy is applied to a high-order spectral model and used to investigate ship-induced waves and wave resistance over a both flat and variable topography. We explore various parameters in our study, including the average water depth to ship draft ratio ( $h_0/d$ ), the channel width to ship width ratio ( $W/B$ ), the Froude number ( $Fr_0=U/\sqrt {gh_0}$ ) and variations in bathymetric slope. Compared with experiments on a flat bottom, the numerical results with dynamic correction show better accuracy in the simulation of ship-induced waves and wave resistance than those obtained using a static pressure distribution. The correlation coefficient for wake waves between the numerical and experimental results is improved by approximately 0.25 with the dynamic correction strategy. The amplitude and wavelength of ship-induced mini-tsunamis over a variable topography are found to be reduced when employing a dynamic correction compared with a static pressure distribution, and this effect becomes more pronounced with higher Froude number. The static pressure approach is shown to allow large deformations of the desired hull shape and changes in ship volume which are responsible for the different wave patterns from the two approaches.

A solution for the automatic detection of expansion joints in dam stilling pools using underwater robots

This research addresses the critical challenges of detecting and measuring underwater concrete structure expansion joints (CSEJ), with a particular focus on dam stilling basin environments. A full set of algorithms for detecting expansion joints with underwater robots is presented by the research, enabling real-time and offline analysis along with visualization. These algorithms introduce the use of Convolutional Neural Networks (CNNs) for image feature extraction and segmentation in CSEJ detection, significantly improving accuracy and adaptability over traditional methods. An automated approach known as the Underwater Dam Crack Detection Network (UDCD Network) was introduced by this research. And advanced attention gates and hybrid dilated convolutions to enhance its detection capabilities are incorporated in this system. Additionally, a three-phase deep transfer learning strategy during training was utilized in this research, which significantly improves the model's performance. The research suggests an underwater image restoration algorithm that uses a dark channel prior and dynamic white balance correction to improve image clarity. Furthermore, unique underwater environmental challenges were tackled by this research, such as non-contact measurement of expansion joint widths using a view-geometry-based algorithm and comprehensive detection of dam wall surfaces. Experimental validation demonstrates that the advanced imaging technologies of the proposed underwater robots hold significant potential for autonomous detection of expansion joints in dam stilling basins.

Dynamic correction of forest fire spread prediction using observation error covariance matrix estimation technique based on FLC-GRU

BackgroundData assimilation (DA) techniques have played a significant role in improving the prediction accuracy of forest fire spread. The dynamic correction technique weights the predicted and observed values to obtain an analytical value that better reflects the position of the fire perimeter. The weighted importance of each contribution is determined by the magnitude of its associated error. However, as a crucial parameter affecting prediction accuracy, the covariance matrix of observation errors is often inaccurate and neglects its own temporal correlation. This is unfriendly to spread prediction results. To address this issue, we proposed a targeted technique for estimating the observation error covariance matrix (R matrix) based on the Fire Line Convolutional Gated Recurrent Unit (FLC-GRU).ResultsWe integrated this method into the DA framework and validated its applicability and accuracy using Observing System Simulation Experiment (OSSE). Through comparisons with traditional methods, the results indicate that using the FLC-GRU estimated R matrix for correction calculations leads to wildfire prediction locations that are closer to the true values.ConclusionsThe proposed approach learns the covariance matrix directly from time-series observed fire line data, without requiring any prior knowledge or assumptions about the error distribution, in contrast to classical posterior tuning methods. The proposed method significantly improves the rationality and accuracy of R matrix estimation, enhances the utility of observational data, and thereby improves the correction accuracy of forest fire spread predictions. Moreover, the study also demonstrates the applicability of the proposed method within the DA framework.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods

The power prediction accuracy of wind farm cluster (WFC) seriously affects its consumption and the safe and stable operation of power system. The fluctuation of power between wind farms (WFs) significantly affects the wind power ultra-short-term prediction (WPUP) accuracy of WFC. In this regard, this paper proposes a graph deviation attention network (GDAN) considering improved clustering distance and learnable graph structure (LGS) for predicting and correcting the wind power of WFC. And used a weighted distance function combining sequence convergence smoothing effect and correlation to dynamically divide the WFC, and to learn and construct the graph structure. Proposed the GDAN with LGS to mine the convergence correlation of WF sub-clusters and establish power prediction model. Considering the characteristics of load peak and valley periods (LPVP), introduced a power correction coefficient to reduce the error, and used the successive variational mode decomposition (SVMD) to extract its key components to achieve power prediction and correction. The proposed method is applied to the WFC in Western Inner Mongolia, China. Compared with the comparison model before correction, the RMSE, MAE and MAPE are reduced by 4.27 %, 3.55 % and 17.92 % respectively, and the R2 and Pr are increased by 11.87 % and 9.88 % respectively.

A novel dynamic structural adaptive multivariable grey model and its application in China's solar energy generation forecasting

Accurately predicting China's solar energy generation is crucial for energy planning and environmental protection. However, the nonlinearity and complexity of the data pose challenges to prediction. This article proposes a dynamic structural adaptive multivariate grey model to overcome these challenges. The model constructs a dynamic nonlinear correction function with an uncertain structure that can adaptively adjust to the data characteristics. Additionally, the Gaussian function is utilized to derive the time response function, overcoming the mechanism defects present in traditional multivariate grey models. The whale optimization algorithm is applied to optimize the background value of the model, improving accuracy. The model is compatible with 14 classical models via parameter changes and has strong compatibility. The results show that the fitting error and test error of the model are 0.23 % and 1.95 % respectively, which is significantly better than the other 11 methods, indicative of strong robustness and accuracy. The forecast indicates that China's solar energy generation will experience a consistent upward trajectory from 2023 to 2030, with an average annual growth rate of 12.11 %. This study significantly contributes to the advancement of the theory of multivariate grey dynamic prediction and provides essential data support for strategic planning within China's solar power industry.

Three-dimensional deformation monitoring of internal nodes of large-span suspended dome structure using videogrammetry under camera instability

Large-span suspended domes often serve as landmarks for cities or countries. This paper proposes a videogrammetric method for monitoring the 3D deformation of internal key nodes in such structures, even under conditions of camera instability. Utilizing circular targets strategically placed on the measurement area, a dynamic parameter calibration method is developed to estimate the instantaneous position and orientation of cameras relative to the world coordinate system, minimizing camera motion-induced errors. Combined with surface targets on the rigid structure and local coordinate frame transformation, the 3D displacement response of structural internal nodes can be precisely measured using the proposed tracking method. The effectiveness and accuracy of the proposed methods were validated through exhaustive experiments, improving positioning accuracy for the structural internal nodes from 47.24 mm to 1.62 mm after dynamic parameter correction. This methodology offers a low-cost, easily implemented, and non-destructive vision-based solution for the long-term monitoring of large-span suspended domes.

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

Research on Rock Energy Constitutive Model Based on Functional Principle

The essence of rock fracture can be broadly categorized into four processes: energy input, energy accumulation, energy dissipation, and energy release. From the perspective of energy consumption, the failure of rock materials must be accompanied by energy dissipation. Dissipated energy serves as the internal driving force behind rock damage and progressive failure. Given that the process of rock loading and deformation involves energy accumulation and dissipation, the rock constitutive model theory is expanded by incorporating energy principles. By introducing the dynamic energy correction coefficient, according to the law of the conservation of energy, the total energy exerted by external loads on rocks is equal to the energy dissipated through the dynamic energy inside the rocks. A new type of energy constitutive model is established through the functional principle and momentum principle. To validate the model’s accuracy, a triaxial compression test was conducted on sandstone to examine the stress–strain behavior of the rock during the failure process. A sensitivity analysis of the parameters introduced into the model was conducted by comparing the model results, which helped to clarify the innate laws of significance of these parameters. The results indicated that the energy model more accurately captures the non-linear mechanical behavior of sandstone under high-stress loading conditions. The model curve fits the test data to a high degree. The fitting curve was basically consistent with the changing trend of the test curve, and the correlation coefficients were all above 0.90. Compared with other models, the model based on the energy principle not only accurately reflects the rock’s stress–strain curve, but also reflects the energy change law of rock. This has reference value for the safety analysis of rock mass engineering under loading conditions and aids in the development of anchoring and support schemes. The research results can fill in the blanks that exist in the energy method in terms of rock deformation and failure and provide a theoretical basis for deep rock engineering. Moreover, this research can further improve and extend the rock mechanics research system based on energy.

Dynamic local field correction of the one-component plasma

The dynamic local field correction (LFC) of the classical one component plasma is studied with molecular dynamics simulations and analytical theory. Simulation results are obtained for a wide range of frequencies and wavenumbers in the strongly coupled regime. The dynamic LFC generally differs significantly from both the zero and high-frequency limits. The latter is attained at increasingly lower frequencies as the coupling grows. In the long wavelength limit, the frequency dependence of the LFC is studied in detail. Pronounced structure is observed in the vicinity of the plasma frequency and its first harmonic. The results provide access to the plasmon dispersion and are used to test the accuracy of various theoretical approaches. In the low-frequency limit, the simulation data are compared with the predictions of hydrodynamics, which connects the LFC with thermodynamic and transport coefficients. While a direct comparison is typically hindered by the finite size of the simulations, good agreement is found at strong coupling for a relation that connects the imaginary part of the LFC with the viscosity.

Awareness of Current Surgical Practice for Congenital Scoliosis and Optimal Timing of Treatment Among Physicians and Patients' Families in Saudi Arabia.

Congenital scoliosis (CS) is a developmental spinal deformity characterized by an abnormal curvature of the spine, affecting one in 1,000 births. The mainstay of treatment involves either observation or surgery in significant curve progression. The optimal timing of surgical intervention is debated, with early intervention preferred. Therefore, understanding physicians' and patients' families' perspectives is crucial for optimizing surgical outcomes in CS. To assess the awareness and knowledge of physicians and patients' families regarding current, as well as new surgical practices and the optimal timing of treatment for CS. A cross-sectional study was conducted in Saudi Arabia using an online self-administered questionnaire distributed through social media platforms and neurosurgery clinics. Levels of awareness were assessed by a knowledge-scoring system. The study involved 403 participants, primarily patients' families (85.1%, N = 343) and physicians (14.9%, N = 60). The results show that physicians had a significantly higher correct response than patients' families regarding the ideal age for surgical correction of CS, the timing of surgical intervention whether before or after maturity, and the role of conservative management, as evident from statistically significant p-values of <0.001, 0.031, and <0.001, respectively. On the contrary, patients' families excelled in understanding interventions irrespective of symptomatic status if Cobb's angle is 40 degrees or above, with a statistically significant p-value of 0.031. Both groups exhibited a good level of overall knowledge, as evidenced by mean awareness scores of 12.18 and 11.64, respectively. Additionally, physicians had a statistically significanthigher level of awareness compared to patients' families, with a p-value of (0.014). However, both groups demonstrated poor knowledge of the latest techniques, including distraction-based magnetically controlled growing rods (MCGRs), growth-guided modern Luque trolleys, and posterior dynamic deformity correction (ApiFix). The mean awareness score of both physicians and patients' families indicates a good level of knowledge. However, both groups exhibited poor knowledge in relation to the optimal timing of treatment and new surgical techniques.

Dynamic exchange correlation effects in the strongly coupled electron liquid

The first quasiexact path integral Monte Carlo (PIMC) investigation of the dynamic local field correction G̃(q,zl;rs,Θ) in the imaginary Matsubara frequency domain is reported, focusing on the unpolarized uniform electron gas in the warm dense matter and strongly coupled regimes. The impact of dynamic exchange–correlation effects on the static structure factor is investigated. A straightforward explanation is provided for previously reported spurious effects in the so-called [Dornheim , ], where the frequency dependence of the local field correction is neglected. The comprehensive analysis of the asymptotic Matsubara order behavior, the density dependence, and the temperature dependence hints at the intriguing possibility of constructing an analytical four-parameter representation of G̃(q,zl;rs,Θ) valid for a substantial part of the uniform electron gas phase diagram, which would constitute key input for thermal density functional theory simulations. Published by the American Physical Society 2024

A Dynamic Iwan Model to Describe the Impact Failure of Bolted Joints

Due to the nonlinearity of the contact interface, as well as the material, jointed structures exhibit complex mechanical behaviors under impact loading. In order to accurately characterize the dynamic response of a joint, this work presents a nonlinear dynamic model (DICF model). First, the effects of loading velocity, preload and friction coefficient on the displacement–load curve are discussed based on a validated finite element model. Numerical simulation results show that the critical load and critical displacement are linearly related to the normalized logarithmic velocity and linearly related to the normalized preload and friction coefficient. Subsequently, a DICF model that consists of sliding, collision and failure is proposed. The constitutive relations of the model are derived, and dynamic correction functions are introduced to characterize the effects of velocity, preload and friction coefficient. A parameter identification method for the model is also provided. Finally, the DICF model is compared with the finite element simulation results, with an error of 0.43% for quasi-static conditions, a minimum error of 0.17% and a maximum error of −1.41% for impact conditions, in addition to significantly improved accuracy compared to the EC3 model, which indicates that it can effectively capture the behavior of bolted joints under impact loading conditions.

A Redundant Secondary Control Scheme to Resist FDI Attacks in AC Microgrids

Hierarchical control of AC microgrids, despite providing stable voltage, frequency, and optimal power distribution, is susceptible to false data injection (FDI) attacks on its secondary control. Electric power systems in countries such as the United States and Venezuela have suffered cyberattacks. This paper proposes a redundant secondary control method to resist FDI attacks. This method uses distributed generators (DGs) online as redundant units on standby, generating dynamic corrections to the attacked DGs, thus eliminating the attack impact. It effectively addresses the numerical tampering and addition brought by FDI to the frequency and voltage setting values of the secondary control, ensuring the microgrid can still output stable frequency and voltage. The principle of this control method is direct and practical; it doesn't require complex controllers and can respond quickly to FDI attacks. Then, focusing on a microgrid system containing six DGs, the setting values of the microgrid system are tampered by multiple values in different time periods, the simulation results confirm that the proposed method can effectively resist FDI attacks under various scenarios. Finally, FPGA-in-the-loop experiments further verify the effectiveness of the proposed method.