Search Results (321)

The article proposes a novel modulation scheme with a low peak-to-average ratio (PAPR), referred to as offset rotation interpolation modulation (ORIM), which is particularly suitable for low-power consumption and enhanced coverage scenarios in the sixth generation (6G) of wireless communication. ORIM comprises three modulation schemes: I-QPSK, I-BPSK, and I-π/2 BPSK. They are derived from cyclic offsetting, phase rotation, and interpolation, and applied to QPSK, BPSK, and π/2 BPSK, respectively. Simulation results in discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) systems demonstrate that ORIM achieves a lower peak-to-average power ratio (PAPR) than the π/2-BPSK scheme specified in the 5G New Radio (NR) protocol, without incurring any performance degradation in terms of block error rate (BLER). Moreover, with the addition of frequency domain spectrum shaping (FDSS), I-π/2 BPSK demonstrates superior performance over π/2-BPSK in both PAPR and BLER metrics under the TDL-A channel conditions. In addition, the complexity of modulation at the transmitting end or demodulation at the receiving end of ORIM is of the same order of magnitude as that of π/2 BPSK, thereby achieving a certain level of overall performance improvement. Full article

The accuracy of phase unwrapping is a decisive factor in achieving high-precision dimensional measurement using the projected fringe profilometry. However, discontinuities at truncation points inevitably lead to phase jumps, especially when measuring objects with complex hollow features, resulting in significantly increased errors. To address this issue, this paper proposes an adaptive phase correction algorithm based on row and column constraints. First, the algorithm identifies the main normal phase distribution region in each column and interpolates abnormal values deviating from this region, ensuring smooth phase distribution in the column direction. Then, it detects each continuous non-zero segment in every row, locates phase jump positions, and performs local corrections. This approach enhances the overall continuity of the phase map and effectively compensates for phase jump errors. Experimental results demonstrate that the proposed method can effectively suppress phase jumps caused by object edges and hollow regions, achieving an absolute error of less than 0.05 mm in measured step height differences in standard blocks. This provides a reliable phase preprocessing solution for the optical measurement of complex-shaped objects. Full article

A novel fractional-order repetitive control based on phase angle information interpolation is proposed for single-phase LCL-type inverters in this paper. Conventional fractional-order repetitive control typically relies on inaccurate grid frequency information detected by a phase-locked loop or the frequency-locked loop, which may result in a potential degradation in harmonics suppression capability. To address this issue, phase information is investigated to implement the fractional order of the repetitive controller through the linear interpolation method. A major advantage of the proposed scheme lies in that it avoids explicit frequency calculation and reduces sensitivity to frequency estimation fluctuations compared with conventional fractional-order repetitive control, enhancing its frequency adaptability. The stability analysis and the design process for the proposed scheme based on a plug-in-type repetitive control are given. Experimental results support the efficacy and advantages of the proposed control strategy. Full article

To address the building settlement issues induced by an urban geological hazard in a northern city, this study utilizes settlement monitoring data from 16 high-rise buildings. The non-uniform temporal data were processed using the Akima interpolation method to construct a settlement prediction model based on a backpropagation (BP) neural network. The model’s predictive performance was validated against traditional approaches, including the hyperbolic and exponential curve methods, and was further employed to estimate the stabilization time of building settlements. Additionally, spatiotemporal characteristics of settlement behavior under the influence of geological hazards were investigated through a comparative analysis of deformation data across the building group. The results demonstrate that the BP neural network model achieves a 58.3% improvement in predictive accuracy compared to traditional empirical methods, effectively capturing the settlement evolution of buildings. The model also provides reliable predictions for the time required for buildings to reach a stable state. The temporal evolution of building settlement exhibits a distinct three-stage pattern: (1) an initial abrupt phase dominated by rapid water and soil loss; (2) a rapid settlement phase primarily driven by the consolidation of sandy and clayey soils; and (3) a slow consolidation phase governed by the prolonged consolidation of cohesive soils. Spatially, building deformations show significant regional heterogeneity, and the existence of potential finger-like preferential pathways for water and soil loss appears to exert a substantial influence on differential settlements. Full article

Precipitation hardening is the primary mechanism for strengthening 6xxx series aluminium alloys. The characteristics of the precipitates play a crucial role in determining the mechanical properties. In particular, predicting yield strength (YS) based on microstructure is experimentally complex and costly because its key variables, such as precipitate radius, spacing, and volume fraction (VF), are difficult to measure. Physics-based models have emerged to tackle these complications utilising advancements in simulation environments. Nevertheless, pure physics-based models require numerous free parameters and ongoing debates over governing equations. Conversely, purely data-driven models struggle with insufficient datasets and physical interpretability. Moreover, the complex dynamics between internal model variables has led both approaches to adopt heuristic optimisation methods, such as the Powell or Nelder–Mead methods, which fail to exploit valuable gradient information. To overcome these issues, we propose a gradient-based optimisation for the Kampmann–Wagner Numerical (KWN) model, incorporating CALPHAD (CALculation of PHAse Diagrams) and a strength model. Our modifications include facilitating differentiability via smoothed approximations of conditional logic, optimising non-linear combinations of free parameters, and reducing computational complexity through a single size-class assumption. Model calibration is guided by a mean squared error (MSE) loss function that aligns the YS predictions with interpolated experimental data using L2 regularisation for penalising deviations from a purely physics-based modelling structure. A comparison shows that the gradient-based adaptive moment estimation (ADAM) outperforms the gradient-free Powell and Nelder–Mead methods by converging faster, requiring fewer evaluations, and yielding more physically plausible parameters, highlighting the importance of calibration techniques in the modelling of 6xxx series precipitation hardening. Full article

In this article, a novel procedure is developed to properly handle the 3-D mispositioning of the scanning probe in the near-field to far-field (NFtFF) transformations with spherical scanning for quasi-planar antennas under test, which make use of a non-redundant (NR) number of samples. It proceeds through two stages. In the former, a phase correction technique, named spherical wave correction, is applied to compensate for the phase shifts of the collected NF samples, which do not belong to the measurement sphere, due to mechanical defects of the arc, or inaccuracy of the robotic arm employed in the considered NF facility driving the probe. Once the phase shifts have been compensated, the recovered NF samples belong to the set spherical surface, but their positions differ from those prescribed by the adopted NR representation, because of an imprecise control and/or inaccuracy of the positioning system. Thus, the resulting sampling arrangement is affected by 2-D mispositioning errors. Accordingly, an iterative procedure is used in the latter step to restore the NF samples at their exact locations from those determined at the first step. Once the correct sampling arrangement has been retrieved from the 3-D mispositioned one, an optimal sampling interpolation formula is employed to obtain the massive input NF data necessary for the classical spherical NFtFF transformation technique. Numerical results, showing the precision of the NF and FF reconstructions, assessed the efficacy of the developed procedure. Full article

In response to the technical challenges of traditional reservoir inversion techniques in determining inter-well wavelets and estimating geological statistical parameters, this study proposes an artificial intelligence high-resolution reservoir inversion technique based on seismic waveform constraints. This technology integrates multi-source heterogeneous data such as lithology characteristics, logging curves, and seismic waveforms through a deep learning neural network framework, and constructs an intelligent reservoir prediction model with geological and physical constraints. Results demonstrate that the proposed technique significantly enhances prediction accuracy for thin sand layers by effectively extracting high-frequency seismic information and establishing robust nonlinear mapping relationships. Inversion errors of reservoir parameters were reduced by more than 25%, while a vertical resolution of 0.5 m was achieved. Predictions agreed with actual drilling data with an accuracy of 86%, representing an 18% improvement over traditional methods. In practical applications, the technique successfully supported new well placement, contributing to a 22% increase in initial oil production in the pilot area. Furthermore, this study establishes a standardized technical procedure: “Time–Depth Modeling-Phase-Controlled Interpolation-Intelligent Inversion”. This workflow provides an innovative solution for high-precision reservoir characterization in regions with limited well control and complex terrestrial depositional systems, offering both theoretical significance and practical value for advancing reservoir prediction technology. Full article

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm. Full article

Full waveform inversion (FWI) comprehensively utilizes phase and amplitude information of seismic waves to obtain high-resolution subsurface medium parameter models, applicable to both active-source and passive-source seismic data. Passive-source seismic exploration, using natural earthquakes or ambient noise, reduces costs and environmental impact, with growing marine applications in recent years. Its rich low-frequency content makes passive-source FWI (PSFWI) a key research focus. However, PSFWI inversion quality relies heavily on accurate virtual source reconstruction. While multi-dimensional deconvolution (MDD) can handle uneven source distributions, it struggles with irregular receiver sampling. We propose a robust MDD method based on multi-domain stepwise interpolation to improve reconstruction under non-ideal source and sampling conditions. This approach, validated via an adaptive PSFWI strategy, exploits MDD’s insensitivity to source distribution and incorporates normalized correlation objective functions to reduce amplitude errors. Numerical tests on marine and complex scattering models demonstrate stable and accurate velocity inversion, even in challenging acquisition environments. Full article

This work presents a stabilized Nitsche-type Cut Finite Element Method (CutFEM) for simulating two-phase flows with complex interfaces. The method addresses the challenges of capturing discontinuities in material properties and governing equations that arise from implicitly defined interfaces. By employing a Continuous Interior Penalty (CIP) method, Nitsche’s method for weak interface coupling, and Ghost Penalty (GP) terms for stability, the formulation enables an accurate representation of abrupt changes in physical properties across cut elements. A stability analysis and a priori error estimation, utilizing Oseen’s formulation, demonstrate the method’s robustness. At the same time, a numerical convergence study incorporating adaptivity and a best-fit quadratic level-set interpolation validates its accuracy. Finally, the method’s efficacy in mitigating spurious currents is confirmed through the Spurious Current Test, demonstrating its potential for reliable simulation of multi-phase flow phenomena. Full article

Low thermal conductivity of phase change materials (PCMs) remains a major limitation in the design of efficient thermal energy storage systems. Enhancing the thermal performance of PCM storage units is therefore a critical design consideration. Fin geometry plays a pivotal role in improving the heat charging and discharging rates by influencing heat transfer mechanisms, particularly natural convection during melting. This study presents a two-dimensional numerical investigation of novel fin geometries aimed at accelerating the melting process of PCM in a double-pipe heat exchanger. Four fin designs are examined: single-step thickness reduction, double-step thickness reduction, stepwise thickness reduction/expansion, and smooth thickness reduction fins. These configurations are specifically developed to promote natural convection currents in the molten PCM regions adjacent to the fin’s surfaces. The enthalpy–porosity method is employed using ANSYS Fluent 19 to simulate the phase change process. The COUPLED algorithm is used for pressure–velocity coupling, with the PRESTO! scheme applied for pressure interpolation and a second-order upwind scheme adopted for the discretization of transport equations. The results demonstrate that the proposed thickness reduction fins significantly enhance the PCM melting rate by intensifying natural convection currents, driven by localized temperature gradients along the fin surfaces. Full article

The cislunar space navigation satellite system is essential infrastructure for lunar exploration in the next phase. It relies on high-precision orbit determination to provide the reference of time and space. This paper focuses on constructing a navigation constellation using special orbital locations such as Earth–Moon libration points and distant retrograde orbits (DRO), and it discusses the simplification of planetary perturbation models for their autonomous orbit determination on board. The gravitational perturbations exerted by major solar system bodies on spacecraft are first analyzed. The minimum perturbation required to maintain a precision of 10 m during a 30-day orbit extrapolation is calculated, followed by a simulation analysis. The results indicate that considering only gravitational perturbations from the Moon, Sun, Venus, Saturn, and Jupiter is sufficient to maintain orbital prediction accuracy within 10 m over 30 days. Based on these findings, a method for simplifying the ephemeris is proposed, which employs Hermite interpolation for the positions of the Sun and Moon at fixed time intervals, replacing the traditional Chebyshev polynomial fitting used in the JPL DE ephemeris. Several simplified schemes with varying time intervals and orders are designed. The simulation results of the inter-satellite links show that, with a 6-day orbit arc length, a 1-day lunar interpolation interval, and a 5-day solar interpolation interval, the accuracy loss for cislunar space navigation satellites remains within the meter level, while memory usage is reduced by approximately 60%. Full article

This study presents a data-driven methodology to optimize the operational efficiency of a tugboat equipped with a Voith-Schneider Propeller (VSP) based on full-scale fuel consumption and vessel performance data. The objective is to identify optimal combinations of engine RPM and propeller pitch to reduce fuel consumption during low-demand phases without compromising maneuverability. Sea trials were conducted under controlled conditions using a dual flowmeter system and onboard speed measurements. The data enabled the construction of performance curves, efficiency ratios, and interpolated maps of fuel consumption. Optimal configurations were identified across defined speed ranges, and continuous efficiency zones were visualized through iso-consumption and contour plots. The results reveal a nonlinear relationship between propeller pitch, speed, and fuel demand, with maximum efficiency occurring at medium-to-high pitch values and speeds between 3 and 6 knots. This methodology provides a replicable tool for energy management in port operations and supports informed decisions during accompanying operations and standby periods. Efficiency differences over 300% between RPM–pitch settings were found, highlighting the operational impact of informed configuration choices. Moreover, the structured dataset and visual analysis framework lay the groundwork for future digital twin models aimed at enhancing operational efficiency in VSP-powered tugboats. Full article

Gas–water two-phase flow in horizontal and inclined wells is significantly influenced by gravitational forces and spatial asymmetry around the wellbore, resulting in complex and variable flow patterns. Accurate measurement of water holdup is essential for analyzing phase distribution and understanding multiphase flow behavior. Water holdup imaging provides a valuable means for visualizing the spatial distribution and proportion of gas and water phases within the wellbore. In this study, air and tap water were used to simulate downhole gas and formation water, respectively. An array capacitance arraay tool (CAT) was employed to measure water holdup under varying total flow rates and water cuts in a horizontal well experimental setup. A total of 228 datasets were collected, and the measurements were processed in MATLAB (2020 version) using three interpolation algorithms: simple linear interpolation, inverse distance interpolation, and Lagrangian nonlinear interpolation. Water holdup across the wellbore cross-section was also calculated using arithmetic averaging and integration methods. The results obtained from the three imaging algorithms were compared with these reference values to evaluate accuracy and visualize imaging performance. The CAT demonstrated reliable measurement capabilities under low- to medium-flow conditions, accurately capturing fluid distribution. For stratified flow regimes, the linear interpolation algorithm provided the clearest depiction of the gas–water interface. Under low- to medium-flow rates with high water content, both inverse distance and Lagrangian methods produced more refined images of phase distribution. In dispersed flow conditions, the Lagrangian nonlinear interpolation algorithm delivered the highest accuracy, effectively capturing subtle variations within the complex flow field. Full article

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article