Smoothing Algorithm Research Articles

A Five-Axis Toolpath Corner-Smoothing Method Based on the Space of Master–Slave Movement

The smoothing of linear toolpaths plays is critical in improving machining quality and efficiency in five-axis CNC machining. Existing corner-smoothing methods often overlook the impact of spline curvature fluctuations, which may lead to acceleration variations, hindering surface quality improvements. The paper presents a five-axis toolpath corner-smoothing method based on the space of master–slave movement (SMM), aiming to minimize curvature fluctuations in five-axis machining and improve surface quality. The concept of movement space in master–slave cooperative motion is introduced, where the tool tip position and tool orientation are decoupled into a main motion trajectory and two master–slave movement space trajectories. By deriving the curvature monotony conditions of a dual Bézier spline, a G2-continuous tool tip corner-smoothing curve with minimal curvature fluctuations is constructed in real-time. Subsequently, using the SMM and the asymmetric dual Bézier spline, a high-order continuous synchronization relationship between the tool tip position and tool orientation is established. Simulation tests and machining experiments show that with our smoothing algorithm, maximum acceleration values for each axis were reduced by 21.05%, while jerk was lowered by 22.31%. These results indicate that trajectory smoothing significantly reduces mechanical vibrations and improves surface quality.

Clinical Validation of a Custom Wearable Patch for Accurate and Comfortable Vital Sign Monitoring in Pediatric Patients

Background/Purpose: Measuring vital signs in pediatric patients requires special consideration and adaptation due to varying anatomy and wide age range. In addition, children's anxiety, uncooperativeness, and high activity levels further complicate measurements, necessitating devices and algorithms designed to minimize the inaccuracies and discomfort. In this work, the performance of a custom wearable patch mounted on the mid-sternum was validated in uncontrolled settings on a cohort including 84 pediatric patients. Methods: Three-minute-long electrocardiogram (ECG), seismocardiogram (SCG) and photoplethysmogram (PPG) signals were acquired using the custom patch. First, pre-processing and signal smoothing algorithms were employed to suppress the out-of-band and motion noise. Two different tasks were then studied: (i) Heart rate (HR) and respiration rate were derived from the ECG, PPG and SCG signals individually. During HR derivation from the SCG, a novel Teager-energy-based HR estimation algorithm was proposed. (ii) Clinical relevance of the SCG signals was shown through mapping the SCG characteristics to body mass index (BMI) and blood pressure values. Results: While the best HR estimation was achieved through the PPG-infrared signal with an absolute error of 2.22.1 bpm, the best respiration estimation was achieved with PPG-Red signal with an error of 2.62.2 breaths/min. On the other hand, regression models resulted in a minimum of 85% confidence interval, revealing that the SCG characteristics indeed have salient correlation with the BMI and blood pressure values. Conclusion: Overall, such a system can potentially be leveraged in clinical practices to achieve more comfortable and accurate measurements.

Quantitative characterisation of debris cloud-induced pitting damage in AL-Whipple shields of spacecraft using infrared thermography

ABSTRACT Quantitatively evaluating the debris cloud-induced pitting damage in the AL-Whipple structure of spacecraft, is still a challenge, let alone microstructural plastic deformation usually accompanied by geometric cratering defects, which may decrease material stress-carrying capacity and structural life. With this motivation, a dedicated modelling technique is proposed to gain an insight into various modalities (i.e., geometric and plastic defects) of pitting damage-induced thermal modulation for infrared inspection. Then, a sequence of infrared images is obtained and analysed using the gap smoothing algorithm and naive Bayes classification methods. On this basis, a quantitative characterization framework is proposed to characterize the damage modalities and their morphologies. This method is validated using a 3D numerical model which contains pitting damage acquired from the real-world structure. Modality classification and morphology evaluation results are well consistent with the artificial defects, in terms of the modality, diameter, and depth. Then they‘re experimentally corroborated, in which the severity and distribution of pitting damage are precisely characterized, in terms of the crater number and morphologies, as well as the size of plastic regions around these craters, which are consistent with the actual observations. This work proposes a non-destructive strategy for pitting damage evaluation, which is engineering-friendly.

Алгоритм количественной оценки кардиореспираторной синхронизации для характеристики функционального состояния и межсистемных взаимодействий у спортсменов

Introduction. The rhythm of the heart (heart rate, HR) is closely related to the rhythm of breathing. The phenomenon of respiratory sinus arrhythmia (an increase in heart rate during inhalation and a decrease during exhalation) is well known. Cardiorespiratory interactions and synchronization of these two signals are evaluated differently in the literature. The purpose of this work was to propose and test an approach to assess cardiorespiratory synchronization – the calculation of the cross-correlation coefficient between heart rate and respiration curve, for a more objective characterization of an athlete’s bodily states. Methods. A photoplethysmogram and respiration were recorded in 45 healthy athletes (18-25 years old) in three situations: rest, paced breathing at 6 times per minute (resonant frequency) and the performance of a sensorimotor task (tapping with the rhythmic metronome sounds and then reproducing a given rhythm from memory). For the HR and respiration curves, filtering and smoothing algorithms using the Savitsky-Goley method were applied sequentially, and then the cross-correlation coefficient between the two curves was calculated. The spectral parameters of heart rate variability (LF and HF) were also obtained, since during spontaneous breathing its contribution to the HR is reflected in HF band, and when breathing at a resonant frequency, a peak occurs at a frequency of 0.1 Hz in the LF band. Results. The cross-correlation coefficient, as well as the peak of HR spectral power at 0.1 Hz, increases significantly when breathing at a resonant frequency. The relationship between these indicators is most accurately described not by a linear, but by a logarithmic dependence. When performing a sensorimotor task, the so-called respiratory waves in the heart rhythm spectrum (HF) do not change from rest to tapping by a metronome and then to holding the rhythm by memory. At the same time, the cross-correlation coefficient demonstrates significant changes between these situations. In addition, a significant correlation was found between the change in the cross-correlation coefficient and the stability of rhythm maintenance (from memory): an increase in the cardiorespiratory synchronization leads to a decrease in motor rhythm stability. Conclusion. Assessment of cardiorespiratory synchronization due to the peculiarities of both signals (changes in heart rate and respiratory phases) requires preliminary preparation of data arrays: filtering and smoothing the stepped curve of heart rate dynamics. The calculation of the cross-correlation coefficient can be used to assess cardiorespiratory synchronization in real time and is likely to be applicable in short time fragments to assess emotional reactions and other short-term fluctuations in psychophysiological conditions, where the classical method of assessing changes in heart rhythm in the frequency or time domains is not applicable.

The development of a bio-inspired vibration source localization algorithm

Vibration source localization is an interdisciplinary topic that has applications in both engineering and biology. To develop bio-inspired technologies that can be applied in engineering, a vibration source localization algorithm is developed based on a spider dynamic model. The model includes: (i) a point source; (ii) a spider model with 8 legs resting on a non-dispersive substrate that introduces correlated leg inputs. The proposed algorithm first calculates the leg inputs using a backward filter forward smoother algorithm with given leg responses to the excitation from the vibration source; and then estimates the source angle, source distance, wave speed and decay rate through optimization. The optimization utilizes the time and amplitude relationships between the leg inputs and the leg-substrate contact locations. The effectiveness of the algorithm is validated by comparing the algorithm estimations with the actual parameters. Results show that the algorithm gives accurate estimation of the source angle and wave speed usually with errors within 3 deg and 1 m/s, respectively, but cannot estimate the source distance. Two spatially-separated spider-like models are needed for estimating the source distance. The algorithm works well even under low signal-to-noise ratio down to 11 dB.

Research on AIS algorithm based on factor graph inference in Doppler localization

Abstract In this paper, an adaptive incremental smoothing (AIS) algorithm based on factor graph inference is proposed to solve the problem of improving the Doppler positioning accuracy of low-Earth orbit communication satellites. Traditional methods such as iterative least squares (ILS) algorithm and Extended Kalman filter (EKF) have certain limitations in real-time Doppler localization. The AIS algorithm uses factor graph inference to identify interference factors, and adopts adaptive smoothing to reduce the impact of estimation errors and promote adaptive noise estimation. The ground station Starlink satellite constellation static positioning was taken as an example, and the simulation was carried out on the STK software simulation platform to verify the feasibility and effectiveness of the algorithm. The experimental results show that the positioning accuracy of the AIS algorithm based on factor graph inference is significantly improved compared with the ILS and EKF algorithms in real-time Doppler positioning solutions. The aim of this research is to improve the positioning accuracy of low-orbit satellites and provide a reference for practical applications.

Disaggregation and Nowcasting of Regional GDP Series with a Simple Smoothing Algorithm

We propose a simple smoothing method for the spatiotemporal disaggregation of economic time series. Contrary to the existing methods, our approach does not require exogenous regressors and can therefore be used for countries lacking long and reliable series of regional economic indicators. The proposed method can also be applied sequentially, implying that one needs to revise only the estimates for the last low-frequency period when new data are disaggregated. This is a convenient feature when historical estimates are of interest. We apply this method to disaggregate annual real GDP data for Polish regions into quarterly series and compare the results with the series obtained from a multivariate linear regression-based procedure, considering the differences between the estimates and the consequences for regional recession dating. We also examine the nowcasting performance of the smoothing algorithm and find that it is superior to the regression-based alternative for most of the studied sample lengths and horizons up to a year.

Low-Cost Real-Time Localisation for Agricultural Robots in Unstructured Farm Environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

Production of Annual Nighttime Light Based on De-Difference Smoothing Algorithm

Production of Annual Nighttime Light Based on De-Difference Smoothing Algorithm

Application of Odd-Order Derivatives in Fourier Transform Nuclear Magnetic Resonance Spectroscopy toward Quantitative Deconvolution.

When Fourier transform (FT) spectrum peaks are overlapped, primary maxima of odd-order derivatives can be used to evaluate their independent intensities. We studied the feasibility of higher odd-order derivatives on Lorentzian peak shape and magnitude peak shape. Simulation studies for FT nuclear magnetic resonance (NMR) spectroscopy demonstrated good results toward quantitative deconvolution of overlapping FT spectrum peaks. Although it is not so desirable to deconvolute special line shapes such as Gaussian, Voigt, and Tsallis profiles, the odd-order derivatives exhibit a bright future compared to even-order derivatives. An application example of practical NMR spectroscopy with ethylbenzene isomers is presented. White Gaussian noises were added to the simulated spectra at two different signal-to-noise ratios (20 and 40). Kauppinen's denoising and smoothing algorithms can effectively remove interference of the noise and help to have good deconvoluting results using the odd-order derivatives. We compared features of our approach with popular deconvolution sharpening algorithms and conducted a comparison study with Kauppinen's Fourier self-deconvolution. Our approach has a better dynamic range of peak intensities and is not sensitive to the sampling rates. Other common deconvolution methods are also discussed briefly.

The 2023 Alaska National Seismic Hazard Model

US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) are used extensively for seismic design regulations in the United States and earthquake scenario development, as well as risk assessment and mitigation for both buildings and infrastructure. This 2023 update of the long-term, time-independent Alaska NSHM includes substantial changes to both the earthquake rupture forecast (ERF) and ground motion models (GMMs). The ERF includes numerous additions to the finite-fault model, considers two deformation models, and introduces updated declustering and smoothing algorithms in the gridded background seismicity model. For the Alaska–Aleutian subduction zone, megathrust earthquakes occur on an updated structural and segmentation model, and the moment magnitude (M) 8+ rupture and rate model include a logic tree branch that considers slip rates derived from geodetic models of interface coupling. The megathrust model considers multiple models of down-dip width, and magnitudes are computed using newly developed scaling relations. For subduction intraslab events and subduction interface events with M < 7, the 2023 update uses a smoothed seismicity model with rupture depths derived from Slab2. The 2023 model updates GMMs in all tectonic settings using the recently published Next Generation Attenuation Subduction (NGA-Sub) GMMs for subduction interface and intraslab events, and the NGA-West2 GMMs for active crustal settings. Collectively, additions and updates to the Alaska NSHM result in hazard increases across most of south-central Alaska relative to the previous model, published in 2007. These changes are primarily due to the adoption of updated rate models for the large-magnitude interface events and the NGA-Sub GMMs that have much higher aleatory variability (sigma), consistent with global observations, and that include models of epistemic uncertainty.

Supervised probabilistic dynamic-controlled latent-variable model for quality pattern prediction and optimisation

A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process.

Adaptive IMM Smoothing Algorithms for Jumping Markov System With Mismatched Measurement Noise Covariance Matrix

Adaptive IMM Smoothing Algorithms for Jumping Markov System With Mismatched Measurement Noise Covariance Matrix

Distance‐Based Smoothing of Curves on Surface Meshes

AbstractThe smoothing of surface curves is an essential tool in mesh processing, important to applications that require segmenting and cutting surfaces such as surgical planning. Surface curves are typically designed by professionals to match certain surface features. For this reason, the smoothed curves should be close to the original and easily adjustable by the user in interactive tools. Previous methods achieve this desired behavior, e.g., by utilizing energy‐minimizing splines or generalizations of Bézier splines, which require a significant number of control points and may not provide interactive frame rates or numerical stability. This paper presents a new algorithm for robust smoothing of discrete surface curves on triangular surface meshes. By using a scalar penalty potential as the fourth coordinate, the given surface mesh is embedded into the 4D Euclidean space. Our method is based on finding geodesics in this lifted surface, which are then projected back onto the original 3D surface. The benefits of this approach include guaranteed convergence and good approximation of the initial curve. We propose a family of penalty potentials with one single parameter for adjusting the trade‐off between smoothness and similarity. The implementation of our method is straightforward as we rely on existing methods for computing geodesics and penalty fields. We evaluate our implementation and confirm its robustness and efficiency.

Spectral induced polarization tomography inversion: Hybridizing homotopic continuation with Bayesian inversion

Induced polarization tomography offers the potential to better characterize the subsurface structures by considering spectral content from data acquisition over a broad frequency range. Spectral induced polarization (SIP) tomography is generally defined as a nonlinear inverse problem commonly solved through deterministic gradient-based methods. To this end, the spectral parameters, i.e., direct current resistivity, chargeability, relaxation time, and frequency exponent, are resolved by individually or simultaneously inverting all frequency data, followed by fitting a generalized Cole-Cole model to the inverted complex resistivities. Due to the high correlation between the Cole-Cole model parameters and a lack of knowledge about the initial approximation of the spectral parameters, using the classical least-squares methods may lead to inaccurate solutions and impede reliable uncertainty analysis. To cope with these limitations, we introduce a new approach based on a hybrid application of a globally convergent homotopic continuation method and a Bayesian inference to reconstruct the distribution of the subsurface spectral parameters. The homotopic optimization, owing to its fast and global convergence, is first implemented to invert multifrequency SIP data sets aimed at retrieving the complex-valued resistivity. Then, Bayesian inversion based on a Markov chain Monte Carlo (MCMC) sampling method, along with a priori information including the lower and upper bounds of the prior distributions, is used to invert the complex resistivity for the Cole-Cole model parameters. By applying the MCMC inversion algorithm, a full nonlinear uncertainty appraisal can be provided. We numerically evaluate the performance of our method using synthetic and real data examples in the presence of topographical effects. Numerical results prove that the homotopic continuation method outperforms the classic, smooth inversion algorithm in the sense of approximation accuracy and computational efficiency. In addition, we determine that our hybrid inversion strategy provides reliable representations of the main features and structure of the earth’s subsurface in terms of the spectral parameters.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Apple firmness detection method based on hyperspectral technology

Firmness is a key indicator of apple quality. Building a predictive model for apple firmness based on hyperspectral technology and regression algorithms can achieve rapid, non-destructive, and high-throughput detection of apple firmness. This paper adopts an Adaptive Window Length Savitzky-Golay Smoothing (AWL-SG smoothing) algorithm based on the Savitzky-Golay Smoothing (SG smoothing) algorithm, which can adaptively adjust the window length according to the change rate of spectral data at different wavelengths. SG smoothing, AWL-SG smoothing, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC) algorithms were used to preprocess the original spectral data, and Partial Least Squares (PLS), Ridge Regression (Ridge), and Kernel Ridge Regression (Kernel Ridge) predictive models were constructed to analyze the impact of different preprocessing methods on model prediction accuracy. The prediction models established with spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms showed significant improvement in predictive performance on the basis of the original spectral data, among which the AWL-SG smoothing algorithm performed the best. The Ridge model established with spectra data preprocessed by AWL-SG smoothing achieved an R2 of 0.8914 in the test set. Successive Projection Algorithm (SPA), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) dimensionality reduction algorithms were used to reduce the dimensions of the full-band spectral data preprocessed by SG smoothing and AWL-SG smoothing algorithms, and Ridge and Kernel Ridge prediction models were constructed. The results showed that both SPA and PCA algorithms could improve the predictive performance of the models, with the PCA performing the best. The combination of AWL-SG + PCA + Ridge achieved the best predictive effect, with an R2 of 0.9146 in the test set.

An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference

In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy.

Combining optimal SASS (Sparsity Assisted Signal Smoothing) and notch filters for transient measurement signals denoising of large power generating unit

Denoising and harmonic interference removal from the measurement signal is crucial for accurate parameters calculation of a generating unit model. Determining the actual parameters values requires various unit transient signals data recorded during a well-defined testing procedure. However, the recorded signals usually contain noise and harmonic interferences which are difficult to remove due to a presence of different kinds of discontinuities.The paper presents a proposal of optimal denoising and harmonic interference removal method from transient measurement signals of 200 MW power generating unit. The method combines notch filters for harmonic rejection followed by SASS (Sparsity Assisted Signal Smoothing) algorithm for wideband noise rejection. Parameters of the notch filters as well as the SASS are determined using popular optimization algorithms (simulated annealing, Nelder-Mead simplex and gradient algorithm).Performance of the proposed method is compared to its direct Linear Time-Invariant (LTI) competitors such as low-pass and notch filters alone, notch and low-pass filters combined and to the optimal SASS method alone. The performance evaluation is based on a set of specially generated test signals which are using the developed mathematical model of the signals (to set the level of noise and interferences) as well as the mathematical model of the power generating unit (to provide the reference clean signals). The obtained results confirm that the optimal notch-SASS method might be a valuable noise removal tool for considered class of signals and leads to superior results than the LTI filters or the SASS method alone.

Twin-Tool Orientation Synchronous Smoothing Algorithm of Pinch Milling in Nine-Axis Machine Tools.

Pinch milling is a new technique for slender and long blade machining, which can simultaneously improve the machining quality and efficiency. However, two-cutter orientation planning is a major challenge due to the irregular blade surfaces and the structural constraints of nine-axis machine tools. In this paper, a method of twin-tool smoothing orientation determination is proposed for a thin-walled blade with pinch milling. Considering the processing status of the two cutters and workpiece, the feasible domain of the twin-tool axis vector and its characterization method are defined. At the same time, an evaluation algorithm of global and local optimization is proposed, and a smoothing algorithm is explored within the feasible domain along the two tool paths. Finally, a set of smoothly aligned tool orientations are generated, and the overall smoothness is nearly globally optimized. A preliminary simulation verification of the proposed algorithm is conducted on a turbine blade model and the planning tool orientation is found to be stable, smooth, and well formed, which avoids collision interference and ultimately improves the machining accuracy of the blade with difficult-to-machine materials.