Computational Efficiency Research Articles

A Robust Tie-Points Matching Method with Regional Feature Representation for Synthetic Aperture Radar Images

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas.

Mechanical property evaluation of 3D multi-phase cement paste microstructures reconstructed using generative adversarial networks

This study proposes an artificial intelligence based framework for reconstructing the 3D multi-phase cement paste microstructure to evaluate its mechanical properties using simulation. The reconstruction of cement paste microstructures is performed using modified generative adversarial networks (GANs) based on microstructural images from micro-CT. For computational efficiency, 2D microstructures are first reconstructed and then extended to 3D microstructures. The reconstructed microstructures exhibit the same microstructural features as the original microstructures when characterized by probability functions. Mechanical properties such as stiffness and tensile strength are evaluated for the original and reconstructed specimens using a phase-field fracture model, and similar behaviors are observed. The results confirm that the reconstructed virtual microstructures can be used to supplement the real microstructures in evaluating the mechanical properties of 3D multi-phase cement paste. This approach thus provides a critical element of a data-driven approach to correlating its microstructure and properties.

A simplified SP3 NEM solver within a unified formulation for pin-by-pin core multi-group calculations

This study addresses the development and verification of a pin-by-pin core multigroup SP3 solver CTRP-Clouds that employs NEM (Nodal Expansion Method) and three simplified NEM methods within a unified formulation for simultaneously solving the coupling 0th and 2nd SP3 equations. In this work, the solver using this unified formulation does not only include the original NEM and its simplifications but also the EFEN (Exponential Function Expansion Nodal) method and FDM (Finite Difference Method) for the comprehensive evaluation. Also, the solver was accelerated using CMFD (Coarse Mesh Finite Difference) method and parallelized using OpenMP. The computational efficiency of different solution methods was investigated for the 2D KAIST benchmark problems and their modified one for considering 3D extension. The results showed the simplified NEM with flat leakage approximation gives acceptable accuracies of less than 1.2 % in RMS of pin-power discrepancies of all the cases with 1x1 mesh per pin-cell, with a reduction of 20 % computing time compared to the original NEM. Particularly, the calculation time of flat leakage NEM is comparable to EFEN while the pin-wise accuracy is better. Besides, the simplified NEM with 2nd-order flux expansion gives substantially improved accuracy in comparison with FDM within comparable computing time.

Prediction of micro wear depth between engineering polymers

In the polymer industry, changes in the wear area that affect surface aesthetics have emerged as a significant issue in applications where surface appearance is crucial. Even minute changes in surface height can be visible to the naked eye, necessitating the need for micro-scale wear prediction of polymers. While the Archard model has been verified for decades for metal-metal or polymer-metal contact, predicting wear between polymer-polymer contacts remains challenging due to limitations in wear measurement and wear coefficient determination. In this study, we propose a method for determining the wear coefficient for polymer-polymer contact and validate the prediction model with a component wear test on polymer parts. Wear depth, an essential indicator of wear is measured after the sliding wear test of the polycarbonate (PC) pin and Acrylonitrile butadiene styrene (ABS) copolymer plate. The wear prediction model with the obtained wear coefficient exhibits 99.7 % accuracy in comparison to the test wear depth. A component wear test is performed on engineering polymer parts, and wear prediction under test conditions is made using the proposed model. The measured and predicted wear depth of the component demonstrates a difference smaller than 5 μm, which is within the range of no change in scratch visibility. This study ensures the accurate wear prediction of engineering polymer components with affordable computational efficiency. The ability to predict polymer-polymer wear, previously unattainable, will enable several applications in the polymer industry, such as component design, scratch visibility prediction, and quality assurance.

Performance analysis of linearization schemes for modelling multi-phase flow in porous media

Reservoir simulation is crucial for understanding the flow response in underground reservoirs, and it significantly helps reduce uncertainties in geological characterization and optimize methodologies for field development strategies. However, providing efficient and accurate solutions for the strong heterogeneity remains challenging, as most of the discretization methods cannot handle this complexity. In this work, we perform a comprehensive assessment of various numerical linearization techniques employed in reservoir simulation, particularly focusing on the performance of the nonlinear solver for problem dealing with fluid flow in porous media. The primary linearization methods examined are finite difference central (FDC), finite forward difference (FDF), and operator-based linearization (OBL). These methods are rigorously analyzed and compared in terms of their accuracy, computational efficiency, and adaptability to changing reservoir conditions. The results demonstrate that each method has distinct strengths and limitations. The FDC method is more accurate particularly in complex simulations where strong heterogeneity are introduced but is generally slower in convergence. The OBL on the other hand, is more efficient and converges quickly, which makes it suitable for scenarios with limited computational resources and simple physics, while the FDF method provides a balanced combination of precision and computational speed, contingent upon careful step size management of the derivative estimations. This paper aims to guide the selection of appropriate linearization techniques for enhancing nonlinear solvers’ accuracy and efficiency in reservoir simulation .

Smart TOPSIS: A Neural Network-Driven TOPSIS with Neutrosophic Triplets for Green Supplier Selection in Sustainable Manufacturing

Decision-making in complex environments requires advanced methodologies to manage uncertainty and indeterminacy effectively. This research introduces a novel decision-making framework that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with Neutrosophic Triplets (NTs) to address the limitations of decision-making methods in handling indeterminate information. By incorporating a frequency analysis-based ranking strategy and leveraging neural network-driven machine learning, the proposed method significantly enhances the accuracy and computational efficiency of the decision-making process. The necessity of this research stems from the growing complexity of multi-attribute decision-making (MADM) scenarios where traditional methods fall short in accurately ranking alternatives under uncertainty. The novelty lies in integrating NTs with a machine-learning approach, providing a more flexible and robust framework for MADM. The proposed method’s contributions are demonstrated through its application in green supplier selection, a critical area in sustainable supply chain management. The results reveal that the smart TOPSIS method improves decision accuracy and reduces computational complexity, making it a viable tool for broader applications. Although the proposed methodology is primarily applied to green supplier selection management, it can also be extended to real-world scenarios in various research fields.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

Pseudopolar Format Matrix Description of Near-Range Radar Imaging and Fractional Fourier Transform

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications.

Distributionally robust origin–destination demand estimation

Gaining a good understanding of the travel demands of a city or region is extremely important for many transportation applications. For stochastic origin–destination (OD) estimation problems, an accurate distribution assumption or observation of OD estimates or data is usually desired but not always available. In this paper, we establish a novel two-stage OD estimation framework based on distributionally robust optimization (DRO) and quasi-sparsity property of large-scale OD demand matrices. The proposed two-stage Distributionally Robust Quasi-Sparsity OD estimation (DR-QSOD) model does not require an accurate or complete distribution assumption of estimates/data. Numerical results demonstrate that DR-QSOD model outperforms stochastic QSOD model in estimating OD demands when the distribution assumption of data is biased. This paper also discusses two different approaches to solve the DR-QSOD model as well as compares their computational efficiency. In addition, DR-QSOD model is shown to keep relatively high quasi-sparsity consistency, which also brings lots of meaningful practical insights.

Pseudopolar Format Matrix Description of Near-Range Radar Imaging and Fractional Fourier Transform

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications.

DoFA: Adversarial examples detection for SAR images by dual-objective feature attribution

Synthetic aperture radar (SAR) classification models based on convolutional neural networks have high accuracy, but the models’ security is still threatened by adversarial examples. The high threat of adversarial examples derives from the invisible noise that can cause feature changes within the model. Among many adversarial examples detection methods, feature attribution that is sensitive to feature changes performs well in feature analysis. Unfortunately, the existing feature attribution-based detection methods cannot balance the computational efficiency and detection performance well due to the size and speckle noise of the images in SAR adversarial examples detection. In this work, we propose the Dual-objective Feature Attribution (DoFA) method by using the feature attribution scan block to find the suitable scan granularity. The DoFA method formulates the SAR adversarial examples detection issue as a dual-objective optimization problem and takes the number of subsamples generated by feature analysis and the area under curve (AUC) value of the logistic regression model as the objective functions while the feature scan block’s size, stride, padding, and the number of selected model layers are the decision variables. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to search for the scan block parameters with Pareto optimality so that the DoFA method can automatically obtain the best feature analysis granularity in different scenarios. The experimental results on the FUSAR-Ship dataset have shown that the proposed DoFA method has a higher AUC value under five adversarial attacks and a smaller number of subsamples than the existing adversarial examples detection method.

Impact of Train Formation on the Dynamic Responses and Operational Safety of High-Speed Trains under Non-Uniform Seismic Ground Motion

The study of dynamic responses and operational safety of high-speed trains under seismic excitation has increasingly relied on numerical simulation as the most effective and convenient research approach. The majority of studies only focus on single-car formation, and fewer utilize train models with actual standard formation, inevitably resulting in differences in dynamic characteristics of train models and simulation results. Furthermore, trains also have different standard formations in different countries and operating scenarios. Therefore, the influences of train formation on the dynamic responses and operational safety of trains under seismic action are investigated. Hence, a detailed train/vehicle–track coupled dynamics model was established to simulate trains under non-uniform seismic ground motion. Moreover, due to wheel–rail contact simulation being the key factor constraining simulation efficiency, based on the influence pattern of train formation, whether fewer train formations can achieve the same simulation accuracy as the 8-car standard formation is also explored in this paper considering seismic wave propagation effect. Results indicate that variation in train formation can influence the dynamic responses of a coupled system significantly from both the perspective of wheel–rail interaction and vehicle kinematic responses. Moreover, the 5-car formation model can better meet the accuracy requirement and significantly improve computational efficiency compared to 8-car formation model.

Automated identification and long-term tracking of modal parameters for a super high-rise building

Structural modal parameter identification plays an important role in wind resistance design, damage identification, and health monitoring. At present, the modal identification process still suffers from low automation and low computational efficiency, which are not conducive to the continuous processing of monitoring data. To address this, an automated modal parameter identification approach is proposed by introducing the fast density and grid based (DGB) clustering, and applied to the Shanghai Tower, which is the tallest building in China. Moreover, in order to determine the optimal model order of the stochastic subspace identification (SSI), the trend of the logarithmic reduction rate for singular values is extracted by empirical mode decomposition (EMD). The numerical simulation of a cantilever beam and the field measurement of Shanghai Tower under the influence of Typhoon In-Fa are conducted to validate the effectiveness of the proposed approach. Results demonstrate that the approach can obtain modal parameters without human intervention and has the advantages of simple process, fast computation and high precision. Furthermore, based on the data collected from the structural health monitoring system of the Shanghai Tower over a period of 5.5 years, the approach is applied to analyze the correlation between natural frequencies and ambient temperature, as well as the long-term evolution law of modes, discovering that multi-order frequencies show a decreasing trend over time. The finite element analysis suggests that this fact might be attributed to the increase in structural mass caused by the rising usage rate and the degradation in material properties.

Control-oriented multiphysics model of a lithium-ion battery for thermal runaway estimation under operational and abuse conditions

This study proposes a control-oriented multiphysics model for lithium-ion batteries (LIBs) that can estimate electrochemical-thermal responses in real time under normal operation and abuse conditions. The proposed model integrates the simplified electrochemical model, the thermal resistance network, and the adaptive time-stepping method to ensure computational efficiency without sacrificing the accuracy. Specifically, the diffusion equation of the electrochemical model is simplified by addressing Padé approximation. The thermal resistance network estimates 3D temperature distribution through simple matrix multiplication to account for the entropic, ohmic, and chemical reaction during thermal runaway. The adaptive time-stepping method further secures accurate yet fast explicit calculation. Quantitative experimental validation reveals the high accuracy and robustness, and fast inference time of the proposed model to estimate the electrochemical-thermal responses with the average inference time of 0.0047 s per step. The application of the proposed model on the 26650 LFP cell also demonstrates the versatility on various shapes and types of LIBs. The systematic analysis on the 3D temperature distribution in the LIB of interest not only confirms the effectiveness of the internal temperature monitoring but also ensures the virtual sensing capability. The versatility of the proposed model underscores both design- and control- enabling solutions for battery thermal management.

Impact of Train Formation on the Dynamic Responses and Operational Safety of High-Speed Trains under Non-Uniform Seismic Ground Motion

The study of dynamic responses and operational safety of high-speed trains under seismic excitation has increasingly relied on numerical simulation as the most effective and convenient research approach. The majority of studies only focus on single-car formation, and fewer utilize train models with actual standard formation, inevitably resulting in differences in dynamic characteristics of train models and simulation results. Furthermore, trains also have different standard formations in different countries and operating scenarios. Therefore, the influences of train formation on the dynamic responses and operational safety of trains under seismic action are investigated. Hence, a detailed train/vehicle–track coupled dynamics model was established to simulate trains under non-uniform seismic ground motion. Moreover, due to wheel–rail contact simulation being the key factor constraining simulation efficiency, based on the influence pattern of train formation, whether fewer train formations can achieve the same simulation accuracy as the 8-car standard formation is also explored in this paper considering seismic wave propagation effect. Results indicate that variation in train formation can influence the dynamic responses of a coupled system significantly from both the perspective of wheel–rail interaction and vehicle kinematic responses. Moreover, the 5-car formation model can better meet the accuracy requirement and significantly improve computational efficiency compared to 8-car formation model.

Determination of dominant oscillation section in power system based on MSSA algorithm and dissipated energy

The identification of dominant oscillation paths is one of the key aspects in studying the stability of inter-area power grids. This paper proposes an improved multi-channel singular spectrum analysis (MSSA) algorithm combined with dissipative energy to identify dominant oscillation section in power systems. First, an improved MSSA method is introduced, which determines the order using the singular value energy spectrum and can effectively identify inter-area oscillation modes in power systems. Second, an energy function analysis method is proposed, which is well-suited for determining dominant oscillation section in power systems. This method is minimally affected by time-varying and nonlinear factors and allows for the visualization of generator states. Finally, the IEEE 68-bus system is used to construct an equivalent model, demonstrating the method's versatility. Simulation results validate the noise resistance, computational efficiency, and applicability to topology change scenarios of the proposed method. This method can accurately analyze inter-area oscillation modes and help to establish the dominant oscillation section.

Discrete Boltzmann model with split collision for nonequilibrium reactive flows* *This work is supported by the National Natural Science Foundation of China (under Grant Nos. U2242214, 51806116 and 91441120), the Guangdong Basic and Applied Basic Research Foundation (under Grant Nos. 2022A1515012116 and 2024A1515010927), the Natural Science Foundation of Fujian Province (under Grant Nos. 2021J01652, 2021J01655), and the China Scholarship Council

A multi-relaxation-time discrete Boltzmann model (DBM) with split collision is proposed for both subsonic and supersonic compressible reacting flows, where chemical reactions take place among various components. The physical model is based on a unified set of discrete Boltzmann equations that describes the evolution of each chemical species with adjustable acceleration, specific heat ratio, and Prandtl number. On the right-hand side of discrete Boltzmann equations, the collision, force, and reaction terms denote the change rates of distribution functions due to self- and cross-collisions, external forces, and chemical reactions, respectively. The source terms can be calculated in three ways, among which the matrix inversion method possesses the highest physical accuracy and computational efficiency. Through Chapman–Enskog analysis, it is proved that the DBM is consistent with the reactive Navier–Stokes equations, Fick's law and the Stefan–Maxwell diffusion equation in the hydrodynamic limit. Compared with the one-step-relaxation model, the split collision model offers a detailed and precise description of hydrodynamic, thermodynamic, and chemical nonequilibrium effects. Finally, the model is validated by six benchmarks, including multicomponent diffusion, mixture in the force field, Kelvin–Helmholtz instability, flame at constant pressure, opposing chemical reaction, and steady detonation.

A novel fast robust optimization algorithm for intensity-modulated proton therapy with minimum monitor unit constraint.

Intensity-modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential. This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint. The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO-NEW) was compared with plans that only consider nominal optimization using CTV (NMO-CTV) without handling uncertainties and PTV (NMO-PTV) to handle the uncertainties, as well as with conventional robust-optimized plans (RBO-CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root-mean-square dose (RMSD)-volume histogram curves (AUC) and dose-volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computationalefficiency. The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO-NEW showing superior computational efficiency and plan quality compared to RBO-CONV. Specifically, statistical analysis results indicated that RBO-NEW was able to reduce the computational time from to s ( ) and reduce the mean organ-at-risk (OAR) dose from % of the prescription dose to % of the prescription dose ( ) compared toRBO-CONV. This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality andreliability.

Correction: Efficient integer division computation protocols based on partial homomorphic encryption

Correction: Efficient integer division computation protocols based on partial homomorphic encryption