Error Reduction Research Articles

Optimization of indoor vehicle ultra-wideband 3D localization using graph attention networks

Ultra-wideband technology is recognized as a low-cost and interference-resistant method for indoor vehicle localization, crucial for advancing intelligent transportation systems. Within this domain, the time difference of arrival algorithm finds extensive application. However, the accuracy of indoor three-dimensional (3D) localization is significantly affected by multipath propagation and non-line-of-sight deviations. To improve localization accuracy, this study proposes an innovative method utilizing the graph attention network framework. This method extracts relevant features and employs attention mechanisms to differentially weight individual nodes; it adeptly captures crucial spatial relationships among nodes, thereby mitigating the effects of multipath propagation and non-line-of-sight deviations, ultimately achieving finer indoor vehicle localization. Experimental results from tests on line-of-sight and non-line-of-sight data sets illustrate the significant improvement in indoor vehicle 3D localization accuracy afforded by the proposed graph attention network–based method. Compared to the commonly used built-in error-state Kalman filter algorithm in commercial ultra-wideband applications and alternative deep learning algorithms, the proposed method achieves error reductions ranging from 60.58% to 84.16% in line-of-sight environments and from 65.59% to 95.21% in non-line-of-sight environments.

Robust iterative value conversion: Deep reinforcement learning for neurochip-driven edge robots

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy’s optimal actions. We verified RIVC’s effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc.

Carbohydrate NMR chemical shift prediction by GeqShift employing E(3) equivariant graph neural networks.

Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectral data. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The model is dubbed GeqShift (geometric equivariant shift) and uses equivariant graph self-attention layers to learn about NMR chemical shifts, in particular since stereochemical arrangements in carbohydrate molecules are characteristics of their structures.

Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features

In previous literature on predicting compressive strength (CS) using machine learning (ML), the focus has primarily been on algorithm-specific improvements, with less emphasis on improving the data perspective of CS prediction. Departing from these conventional investigations, this study presents data-centric enhancements. A systematic and comprehensive methodology is adopted, involving series of steps. Firstly, a large dataset (> 24,214 datapoints) comprising 26 input features relating to mixture proportions, engineered ratios and atmospheric parameters is prepared and processed. Feature engineering is then performed for selection and processing of data features. Subsequently, six scenarios are designed and investigated for CS prediction, focusing on distinct combinations of input features. Finally, CS predictions for all six scenarios are compared using five well-known ML model, and results are validated using statistical hypothesis testing. The results show significance improvements in CS prediction, with a 12.8 % increase in coefficient of determination (R2) and a 61 % reduction in root mean square error (RMSE) achieved by incorporating engineered and atmospheric parameters in conjunction with mixture proportion as inputs in CS prediction. Based on CS prediction in six scenarios, the study presents useful discussions focusing on concrete data perspective, role of engineering ratio and atmospheric features, and interaction and influence of input features in CS prediction. In conclusion, this work emphasizes the importance of enhancing data management practices for concrete performance assessment to have a more efficient, realistic, and sustainable outcomes in the concrete industry.

Multivariate Poisson cokriging: A geostatistical model for health count data.

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions. Variability in population sizes further complicates risk estimation from the counts. This study introduces multivariate Poisson cokriging for predicting and filtering out disease risk. Pairwise correlations between the target variable and multiple ancillary variables are included. By means of a simulation experiment and an application to human immunodeficiency virus incidence and sexually transmitted diseases data in Pennsylvania, we demonstrate accurate disease risk estimation that captures fine-scale variation. This method is compared with ordinary Poisson kriging in prediction and smoothing. Results of the simulation study show a reduction in the mean square prediction error when utilizing auxiliary correlated variables, with mean square prediction error values decreasing by up to 50%. This gain is further evident in the real data analysis, where Poisson cokriging yields a 74% drop in mean square prediction error relative to Poisson kriging, underscoring the value of incorporating secondary information. The findings of this work stress on the potential of Poisson cokriging in disease mapping and surveillance, offering richer risk predictions, better representation of spatial interdependencies, and identification of high-risk and low-risk areas.

Machine learning for aspherical lens form accuracy improvement in precision molding of infrared chalcogenide glass

Precision glass molding (PGM) is an effective approach to manufacturing infrared chalcogenide glass (ChG) aspherical lens with complex shapes. However, infrared ChG aspherical lens often experiences form error in the designed profile and the final profile obtained by PGM. To reduce the form error of infrared ChG aspherical lens in the PGM process, a form error compensation model based on the random forest (RF) algorithm is proposed. The infrared ChG aspherical lens profile was first machined on an electroless nickel-phosphorus (Ni–P) plating to serve as the mold for PGM. After molding, the profile data of the lens was extracted, and a compensation model based on RF was constructed to optimize the model parameters using the evaluation parameters such as root mean square error (RMSE), coefficient of determination (R2), and out-of-bag (OOB). Finally, the generated compensated profile based on the compensation model was used for the compensation machining of the mold. Through this compensation approach, we have demonstrated a substantial 63.5 % reduction in the form error of the fabricated infrared ChG aspherical lens, decreasing the Peak-to-Valley (PV) value from 1.04 μm to 0.38 μm.

Research on high-precision angular measurement based on machine learning and optical vortex interference technology

Abstract The paper innovatively constructs a regression prediction model based on the Stacking ensemble learning algorithm by utilizing the distortion degree of vortex optical interference patterns, achieving high-precision measurement of small angles. It constructs a regression prediction model based on the Stacking ensemble learning algorithm. Initially, in the spiral optical conjugate interference system, minute variations in the optical axis yield corresponding interference patterns, within an angle range of 0.0006° to 0.3°. The angle formed between the centroids of the upper two petals in the deformed interference patterns and the center is extracted as a feature for feature extraction. A dataset is established and randomly divided into training, validation, and testing sets in a 6:2:2 ratio. Subsequently, four models—support vector regression, particle swarm optimization back propagation, Gaussian process regression, and the stacking ensemble algorithm—are optimized for hyperparameters, trained, and evaluated based on coefficients of determination, root mean square error, and mean absolute error to compare their predictive performance. Through multiple rounds of training and prediction on randomly partitioned datasets, it is evident that the ensemble model exhibits a reduction in relative error compared to single learners, demonstrating that the Stacking-based ensemble algorithm can combine the strengths of base learners, showcasing superior predictive performance and enhanced stability. Moreover, the Stacking ensemble model achieves a measurement precision of 0.0006°, with a relative error maintained within 0.6%, indicating the feasibility of achieving high-precision measurement of tiny angles in the optical axis using machine learning and spiral optical conjugate interference systems.

Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter

Accurate flood forecasts are vital for reservoir operation and flood prevention. The unscented Kalman filter (UKF) excels in improving physically-based models flood forecasting but depends on precise noise estimation, posing challenges in complex uncertainty quantification. In this study, we propose a novel approach, the deep fusion of a long short-term memory (LSTM) neural network and unscented Kalman filter (LSTM-UKF), to enhance flood forecasts by adaptively updating hydrological model states. The LSTM efficiently learns noise-related information from the estimation of Kalman Gain, obviating the need for intricate uncertainty recognition and approximation. For comparative analysis, the LSTM-UKF and UKF filters are constructed to correct Xinanjiang (XAJ) hydrological model states. Both filters utilize streamflow observations to update the XAJ model states and facilitate its flood forecasting performance. Comprehensive evaluations were conducted using 3-hourly meteor-hydrological sequences from the Jianxi and Tianyi basins in China, focusing on the accuracy, stability, and reliability of multi-step-ahead flood forecasts. Results indicate that the LSTM-UKF performs superior to the UKF, with maximal advancements in Nash-Sutcliffe efficiency coefficient (NSE) by 9.1%, maximal reduction in root mean square error (RMSE) by 18.7%, and maximal decrease in mean absolute error (MAE) by 22.6%. Additionally, the LSTM-UKF exhibits better robustness and stability in non-stationary flood events. The proposed LSTM-UKF mitigates the over-reliance on noise estimates, reducing systematic biases and error accumulation in state estimates and enhancing hydrological model generalizability in operational flood prevention platforms and systems.

Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Due to its strong dependence on weather conditions, photovoltaic (PV) power is highly intermittent in nature. In light of this, this paper introduces a PV power prediction model based on similar-day clustering and temporal convolutional network (TCN) with bidirectional long short-term memory (BiLSTM) model. Firstly, in the data preprocessing stage, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is used to decompose and denoise the original PV power data to obtain subsequences of different frequencies. Subsequently, the sample entropy (SE) method is incorporated to reconstruct the subsequences and generate low-frequency and high-frequency subsequences with more pronounced temporal features. In addition, a self-organizing map (SOM) with the K-means algorithm is used to categorize historical days into four weather types: sunny, cloudy, rainy, and intermittent. Through grey relational analysis (GRA), meteorological factors significantly affecting PV power prediction are identified to construct a multidimensional feature set. During the model prediction phase, historical PV power data and related meteorological impact factors are input into the TCN-BiLSTM model to perform multi-data-driven PV power prediction. Finally, the predicted results of each subsequence are linearly combined to obtain the ultimate PV power prediction. The proposed model is compared with convolutional neural network (CNN)-LSTM, LSTM, and TCN models, achieving a reduction in the root mean square error (RMSE) of 0.129 kW, 0.238 kW, and 0.257 kW, respectively. This demonstrates that the proposed model exhibits superior overall performance in time series modeling and information capture, enhancing the understanding of seasonal, periodic, and irregular patterns in PV power generation data.

S-wave log construction through semi-supervised regression clustering using machine learning: A case study of North Sea fields

Accurate prediction of S-wave velocity from well logs is essential for understanding subsurface geological formations and hydrocarbon reservoirs. Machine learning techniques, including clustering and regression, have emerged as effective methods for indirectly estimating S-wave logs and other rock properties. In this study, we employed clustering algorithms to identify similarities among well log datasets, encompassing depth, sonic, porosity, neutron, and apparent density, facilitating the discovery of correlations among various wells. These identified correlations served as a foundation for predicting S-wave values using a novel semi-supervised approach. Our approach combined clustering, specifically k-means clustering, with different types of regressors, including Least Squares Regression (LSR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Our results demonstrate the superior performance of this integrated approach compared to traditional regression methods. We validated our methodology using various parametric and non-parametric regression techniques, showcasing its effectiveness not only on wells within the training region but also on wells outside the study area. We achieved a significant improvement in the R2 score metric, ranging from 2.22% to 6.51%, and a reduction in Mean Square Error (MSE) of at least 31% when compared to predictions made without clustering. This study underscores the potential of machine learning techniques for accurate prediction of S-wave velocity and other rock properties, thereby enhancing our comprehension of subsurface geology and hydrocarbon reservoirs.

Correcting Dispersion Corrections with Density-Corrected DFT.

Almost all empirical parametrizations of dispersion corrections in DFT use only energy errors, thereby mixing functional and density-driven errors. We introduce density and dispersion-corrected DFT (D2C-DFT), a dual-calibration approach that accounts for density delocalization errors when parametrizing dispersion interactions. We simply exclude density-sensitive reactions from the training data. We find a significant reduction in both errors and variation among several semilocal functionals and their global hybrids when tailored dispersion corrections are employed with Hartree-Fock densities.

2259 Assessing antibiotic usage on a geriatric ward

Abstract Introduction The World Health Organisation lists antibiotic resistance as one of the biggest threats to global health [1]. We contribute to this as clinicians, through errors such as delayed review of prescriptions or prescribing against local trust guidelines. Method We have carried out a quality improvement project to improve antibiotic prescriptions on a geriatric ward at Croydon University Hospital. We carried out a fortnightly cross-sectional analysis of the antibiotic prescriptions on a geriatric ward. This included looking at the antibiotic prescribed, indication, duration, route of administration and presence of a review date. These were then compared to trust guidelines. After the first 8-weeks, we delivered a departmental teaching session on antibiotic prescriptions. We then re-audited the prescriptions. Following this, we sent out weekly email reminders on locating trust guidelines and information on prescriptions. We then re-audited following this. Finally, we created an e-learning resource to deliver to the ward on antibiotic prescriptions. We are planning to deliver this to the ward and re-audit afterwards. Results Initially, up to 90.0% of prescriptions differed from trust guidelines. Common reasons for differences when compared included incorrect drug prescribed, incorrect frequency of dosing, or non-specific indications leading to difficulty comparing. Following all interventions, approximately 32% of prescriptions differed from trust guidelines. This showed sustained improvement across 2 complete PDSA cycles (plan, do, study, act). A 3rd PDSA cycle is ongoing at present and preliminary data has shown approximately 28% of prescriptions differed from trust guidelines. Conclusion This quality improvement project has successfully contributed to a reduction in prescription errors and safe prescribing. We will continue to provide information to our colleagues on antibiotic stewardship, to further encourage safe prescribing. [1]. Antibiotic resistance (2020) World Health Organisation. Available at: https://www.who.int/news-room/fact-sheets/detail/antibiotic-resistance (Accessed: May 2023).

Trajectory control and optimization of PID controller parameters for dual-arms with a single-link underwater robot manipulator

ABSTRACT This paper focuses on the modeling, simulation, and control of the trajectories of a dual-arm single-link underwater robot manipulator (DS-URM), having dual arms (left and right) with a single link each. The dynamic coupling between the base and the dual arms makes trajectory control of the end effector difficult. This study attempts to simulate and control dual arms with a single-link underwater robotic manipulator using a hybrid controller. To reduce the error for a sinusoidal wave and a cycloid trajectory, a hybrid controller is the combination of the Proportional-Integral-Derivative (PID) controller and overwhelm controller. The underwater dynamic’s buoyancy, gravity, and hydrostatic forces acting on the underwater robot are modeled in an integrated SIMULINK model, including the DS-URM. The DS-URM has been investigated, revealing some significant trajectories of the left and right tips. Effective tuning methods include Ziegler-Nicholas (Z-N), genetic algorithms, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). The Integral of Time multiplied by Absolute Error (ITAE) criteria has been used to improve trajectory tracking via particle swarm optimization. Results show significant improvements in trajectory tracking, with ITAE error reduction by 96.44% and 98.6% for left and right arms, respectively, achieving stability in 0.000645s.

Enhanced single-frame interferometry via hybrid conv-transformer architecture for ultra-precise phase retrieval

Precise dynamic single-frame interferometry based on virtual phase shifting technique remains challenging due to the difficulty in satisfying the requirements for the quality and amount of fine-grained fringe’s interferograms. Here we introduce a novel deep learning architecture, the Transformer Encoder-Convolution Decoder Phase Shift Network (TECD-PSNet), that achieves high-fidelity interferogram reconstruction. TECD-PSNet seamlessly integrates the strengths of transformer blocks in capturing global descriptions and convolution blocks in efficient feature extraction. A key process is the incorporation of a residual local negative feedback enhancement mechanism that adaptively amplifies losses in high-error regions to boost fine-grained detail sensitivity. This approach enables accurate phase retrieval for diverse pupil shapes, enhancing adaptability to various optical setups, while significantly reducing the amount of training data required. Experiments demonstrate a 22.9% improvement in PSNR for reconstructed interferograms and a 36.7% reduction in RMS error for retrieved phases compared to state-of-the-art methods.

Real-time motion-induced error reduction for phase-shifting profilometry with projection points tracking method

Fringe projection profilometry is a significant method for three-dimensional measurement due to its non-contact and high accuracy. However, the motion-induced error will lead to the loss of measurement accuracy in dynamic scenes due to the disruption of the phase measurement process. In this paper, we introduce a novel motion error model that considers object motion causes the misalignment of the projection points on the camera, and propose the projection points tracking method to reduce the motion-induced error. First, the speckle pattern is added to projection sequences, after which the projection point displacements are determined using the digital image correlation (DIC) method between the adjacent speckle patterns. Finally, the fringe patterns are corrected using the image remapping method to calculate the 3D shape. Quantitative analysis and dynamic measurement experiments verify the feasibility of the proposed method. Different from 3D-DIC, we use one camera-projector system to realize high-accuracy measurement in dynamic scenes.

Bayesian-informed fatigue life prediction in shallow shell structures with the dual boundary element method

This study introduces an innovative approach for statistically inferring the Equivalent Initial Flaw Size Distribution (EIFSD) in shallow shell structures with the aim of predicting fatigue life. The proposed approach integrates Bayesian inference and surrogate modelling techniques, and utilizes a long crack growth model. The EIFSD is crucial for estimating fatigue life and ensuring structural durability. To showcase the proposed methodology, a numerical example featuring a fuselage window structure under multi-axial loading, employing the Dual Boundary Element Method (DBEM) for crack growth modelling, is presented. Efficiency gains in the inference of the EIFSD are achieved through the development of surrogate models, notably a Co-Kriging model that significantly reduces computational time and outperforms a Kriging model in error reduction. The study explores two assumptions for inspection data sourcing, leading to enhanced methodological effectiveness across varying uncertainty levels. It considers the separate effects of Paris Law constants and loading conditions under increased uncertainty, presenting cases that demonstrate the Co-Kriging model’s high accuracy and computational efficiency. The proposed methodology’s robustness is further validated by assessing the impact of prior distribution quality on Bayesian inference, showing the method’s adaptability to high uncertainty while maintaining a good level of accuracy. This study also demonstrates an approach for fatigue life estimation of structures using the inferred EIFSD. By evaluating the structure up to a critical crack length, the methodology allows for the accurate estimation of fatigue life, which, in turn, facilitates the determination of optimal inspection intervals. Notably, under conditions of high uncertainty, the method showcased a maximum difference of 1.51% in the estimated fatigue life when compared to the true EIFSD, underscoring the technique’s high accuracy and reliability.

ANCF-based dynamic modeling of variable curvature soft pneumatic actuators with experimental verifications

Accurate and computationally efficient models of soft pneumatic actuators are crucial for utilizing their compliance in various fields. However, existing research primarily relies on the piecewise constant curvature assumption or the quasi-static assumption, only valid in limited situations. In this paper, we present a dynamic model based on absolute nodal coordinate formulation (ANCF) that simultaneously accounts for variable curvature deformation and dynamic properties. To this end, deformed configurations of soft pneumatic actuators are firstly discretized into ANCF-based beam elements. Based on this parameterization method, the dynamic model is derived by the principle of virtual work. After identifying model parameters, Newmark algorithm is utilized to solve the dynamic model in real-time, averagely consuming 6.76 s of a 10 s simulation. The derived dynamic model is experimental verified using a soft pneumatic actuator. The experimental results demonstrate that the maximum simulation errors of the tip remain below 2.5% of the actuator’s length when the actuator is subjected to various pressure and tip loads. In addition, the overshoot behavior and period of vibration in the oscillations are also predicted by the dynamic model. Moreover, the dynamic model exhibits an average 46.53% reduction in simulation error compared with the static ANCF-based model. Overall, this work paves the way to a deeper insight to dynamic motion analysis of soft pneumatic actuators.

Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin

AbstractSatellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.

Differentially Private Stream Processing at Scale

We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system - Differential Privacy SQL Pipelines (DP-SQLP) - is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google. Towards designing DP-SQLP we make both algorithmic and systemic advances, namely, we (i) design a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys, and can scale to one billion keys that users have contributed, (ii) design a preemptive execution scheme for DP key selection that avoids enumerating all the keys at each triggering time, and (iii) use algorithmic techniques from DP continual observation to release a continual DP histogram of user contributions to different keys over the stream length. We empirically demonstrate the efficacy by obtaining at least 16× reduction in error over meaningful baselines we consider. We implemented a streaming differentially private user impressions for Google Shopping with DP-SQLP. The streaming DP algorithms are further applied to Google Trends.

Advancing the Local Pulse Wave Velocity Measurement-Wave Confluence Decomposition Using a Double Gaussian Propagation Model.

Background Pulse wave velocity (PWV) is a marker of arterial stiffness and local measurements could facilitate its widescale clinical use. However, confluence of incident and early reflected waves leads to biased spatiotemporal PWV estimates. Objective We introduce the Double Gaussian Propagation Model (DGPM) to measure local PWV in consideration of wave confluence (PWV[Formula: see text]) and compare it against conventional spatiotemporal PWV (PWV[Formula: see text]), with Bramwell-Hill PWV (PWV[Formula: see text]) and blood pressure (BP) as reference measures. Methods Ten subjects ranging from normotension to hypertension were repeatedly measured at rest and with induced PWV changes. Carotid distension waveforms over a 19 mm wide segment were acquired from ultrasonography, simultaneously with noninvasive continuous BP. Per cardiac cycle, the 8-parameter DGPM (amplitude, centroid, width, and velocity, respectively of forward and backward propagating wave) was fitted to the distension waveforms' systolic foot and dicrotic notch complexes. Corresponding PWV[Formula: see text] was computed from linear fittings of respective feature timings and distances. Regression analyses were conducted with PWV[Formula: see text] and PWV[Formula: see text] as predictors, and various PWV and BP measures as response variables. Results Whereas PWV[Formula: see text] correlations were insignificant, PWV[Formula: see text] estimated the reference PWV[Formula: see text] with a significant reduction in errors (P < 0.001), explained up to 65% PWV[Formula: see text] variability at rest, demonstrated higher intra-method consistency and correlated significantly with all BP measures (P < 0.001). Conclusion The proposed DGPM measures local carotid PWV in consideration of wave confluence, showing significant correlations with Bramwell-Hill PWV and BP at two distinct waveform complexes. Thereby PWV[Formula: see text] outperforms the conventional PWV[Formula: see text] in all investigated respects, potentially enabling PWV assessment in routine clinical practice.