High Accuracy Of Method Research Articles

An improved high-accuracy interpolation method for switching devices in EMT simulation programs

This paper presents an interpolation with Backward Euler (BE) method that enhances the switching simulations accuracy in an electromagnetic transient (EMT) simulation with a fixed time-step. Because of the switching operations on discrete instants, the simulations can show artificial voltage spikes and numerical oscillations (chatter), compromising the accuracy and producing misleading results. We thus present a method that eliminates the spurious switching losses and improves the accuracy by combining interpolation and extrapolation with a half-time-step BE solution over existing interpolation-based approaches to resolve the issues above. In addition, this improved method requires the same calculation steps as the industry-accepted instantaneous interpolation. Analytical discussion reinforced by the simulation studies for a simple and complex power electronic circuits demonstrates the accuracy and efficacy of the proposed interpolation with BE method.

Surrogate-assisted high-accuracy observation modeling in building digital twins: In situ nonintrusive modeling without sensor observation (Y)

Observation and prediction models play important roles in intelligent building systems in terms of holistic monitoring and optimal control. These models overcome the limitations of physical sensors and contribute to organizing the building sensing environment into a more informative arrangement. However, developing an in-situ prediction model for real systems is challenging, as the target sensor observation (Y) is absent. Although physics-based white-box modeling is possible, the model observations have operational uncertainties in a real system. Therefore, this study proposes a novel and integrated framework for a nonintrusive high-accuracy observation modeling method including (1) white-box modeling, (2) indirect-nonintrusive calibration, and (3) surrogate-based uncertainty reduction. In contrast to the conventional application purpose of a surrogate model, the proposed method adopts surrogate modeling to reduce the model uncertainty and provide high accuracy. The proposed method is applied to a real building system to evaluate the accuracy of each modeling step in the proposed framework. The initial white-box model shows an error of 0.81 °C, indirect-nonintrusive calibration improves the accuracy with an error of 0.55 °C, and the surrogate-assisted model reaches an error of 0.27 °C. Notably, 51% of sensor errors are decreased through surrogate modeling.

Rapid one-shot dual-shearing digital shearography using a spatial light modulator.

Digital shearography is a non-contact, whole-field, and high-accuracy laser-based optical interferometric method. It is widely used in the field of non-destructive testing and material evaluation. Dual-shearing DS, as the state-of-the-art method, can detect defects or measure the derivative of deformation in two sensitive directions. Most existing dual-shearing DS is realized with a bulky Michelson or Mach-Zehnder interferometer; recently, the usage of a spatial light modulator (SLM) in DS offers a new approach to designing a simple and light shearographic system. However, prior proposed SLM-based DS requires multiple shots for the phase map acquisition, with the classic temporal phase-shift (TPS) technique severely limiting its applications. This paper proposes a novel, to our best knowledge, one-shot dual-shearing DS by creating a dual-stripe pattern in the SLM. Two separate phase maps, with different sensitive directions, were acquired simultaneously via the spatial phase-shift technique. The measurement can be easily done within a single shot in its compact and light body. Moreover, the shearing amount of the two sensitive directions can be set independently and precisely. These advantages promote that the proposed system can be applied in various applications, especially for dynamic and complicated composite material testing. A detailed theory and experimental validation are described.

A new high-accuracy difference method for nonhomogeneous time-fractional Schrödinger equation

The fractional Schrödinger equation is an important fractional nonlinear evolution equation, and the study of its numerical solution has profound scientific meaning and wide application prospects. This paper proposes a new high-accuracy difference method for nonhomogeneous time-fractional Schrödinger equation (TFSE). The Caputo time-fractional derivative is discretized by high-order formula and the fourth-order compact difference approximation is applied for spatial discretization. A new nonlinear compact difference scheme with temporal second-order and spatial fourth-order accuracy is constructed, which is solved by the efficient linearized iterative algorithm. The unconditional stability and convergence are analysed by the energy method. The unique existence and maximum-norm estimate of new compact difference scheme solution are obtained. Theoretical analysis shows that the convergence accuracy of new compact difference scheme is with the strong regularity assumption. Numerical experiments verify theoretical results and indicate that the proposed method is an efficient numerical method for solving TFSE.

Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes

The release of 150,119 UK Biobank sequences represents an unprecedented opportunity as a reference panel to impute low-coverage whole-genome sequencing data with high accuracy but current methods cannot cope with the size of the data. Here we introduce GLIMPSE2, a low-coverage whole-genome sequencing imputation method that scales sublinearly in both the number of samples and markers, achieving efficient whole-genome imputation from the UK Biobank reference panel while retaining high accuracy for ancient and modern genomes, particularly at rare variants and for very low-coverage samples.

A Discontinuous Petrov-Galerkin Method for One-Dimensional Hyperbolic Conservation Law Equations Based on HWENO Limiter

The discontinuous Petrov-Galerkin method (DPG) is a new type of numerical solution to the hyperbolic conservation law equations, which distinguishes itself from the DG method by its high accuracy based on compact stencils. However, in order to overcome the unphysical oscillations of the higher order linear schemes near the large gradient solution, the DPG method often needs to incorporate limiter functions to obtain a high-resolution image of the numerical solution. This paper attempts to combine HWENO as limiter function with DPG to solve the discontinuous initial value problems for the hyperbolic conservation law equations. The single-step high-accuracy SSP Runge-Kutta method is used for time discretization, and a new HWENO-based process is used as the limiter of the RKDPG methods, which requires only one reconstruction to complete the update of the higher-order moments without calculating the linear weight coefficients. . Since the accuracy does not meet the design requirements, the HWENO limiter above is partially improved for the identification of the problem cells in the HWENO limiter above, with the original numerical solution used at the smooth solution. This paper only gives the calculation results of P1 ~P3, and the limiter is also applicable to the DPG method for higher elements. Numerical examples show that the HWENO limiter can effectively suppress non-physical oscillations in the problem cells and keep the original accuracy at the non-problem cells. The high accuracy and compactness characteristics of the DPG method are maintained. The numerical calculation solutions are efficient and accurate.

Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals

Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming. Alternatively, the machine learning (ML) method shows the strength in predictive performance. In this study, using the formation of a typical montmorillonite-bound phenoxy radical as an example, a generalized ML framework RAPID was proposed for activation energy prediction of environmental interfacial reactions. Accordingly, an explainable ML model was developed to predict the activation energy via easily accessible properties of the cations and organics. The model developed by decision tree (DT) performed best with the lowest root-mean-squared error (RMSE = 0.22) and the highest coefficient of determination values (R2 score = 0.93), the underlying logic of which was well understood by combining model visualization and SHapley Additive exPlanations (SHAP) analysis. The performance and interpretability of the established model suggest that activation energies can be predicted by the well-designed ML strategy, and this would allow us to predict more heterogeneous transformation reactions in the environmental field.

Artificial neural network-based investigation on high-speed train-induced embankment vibration in frozen regions

High-speed trains (HSTs) induce vibrations that deteriorate the embankments of ballastless tracks, particularly in frozen regions, necessitating vibration assessments for existing high-speed railways (HSRs). This study developed an indirect thermal-dynamic coupled two-dimensional (2D) finite element (FE) model to investigate the dynamic responses of the embankment of ballastless tracks to various subgrades and subsoils, train speeds, and frost depths in frozen regions. The results indicate that subgrade is the key parameter to analysing the dynamic response of the train–track–ground system considering train speeds. A fast and high-accuracy prediction method for the vibration responses was developed using an artificial neural network (ANN) and used to predict the vibration level under different conditions. Prediction results determined train speeds under 400 km/h safe for HSRs. This study can improve knowledge of ballastless track substructure design and extend the applicability of the current railway code to higher train speed conditions in frozen regions.

Analysis of a High-Accuracy Numerical Method for Time-Fractional Integro-Differential Equations

A high-order finite difference numerical scheme based on the compact difference operator is proposed in this paper for time-fractional partial integro-differential equations with a weakly singular kernel, where the time-fractional derivative term is defined in the Riemann-Liouville sense. Here, the stability and convergence of the constructed compact finite difference scheme are proved in L∞ norm, with the accuracy order O(τ2+h4), where τ and h are temporal and spatial step sizes, respectively. The advantage of this numerical scheme is that arbitrary parameters can be applied to achieve the desired accuracy. Some numerical examples are presented to support the theoretical analysis.

Local discontinuous Galerkin method for the Riesz space distributed-order Sobolev equation

We present and analyze a local discontinuous Galerkin finite element method to solve the Riesz space distributed-order Sobolev equation. To approximate the distributed-order Riesz space derivative, the Gauss quadrature as the high computational accuracy method is proposed. A multi-term fractional equation is then constructed from the considered equation by approximating the Riesz space derivative. Moreover, stability analysis is provided for a semi-discrete scheme. We provide numerical results to justify the theoretical analysis by solving the ordinary differential equation. We obtain this after implementing the local discontinuous Galerkin scheme on the Riesz space distributed-order Sobolev equation with the Crank–Nicolson scheme as a time marching method and the Laplace transform technique.

High-accuracy measurement system for rotor-stator axial clearance in narrow spaces based on all-fiber microwave photonic mixing.

In this paper, a high-accuracy measurement method for rotor-stator axial clearance in narrow spaces is proposed. The optical path structure based on all-fiber microwave photonic mixing is established. To improve the accuracy and expand the measurement range, the total coupling efficiency over the entire measurement range at different working distances of fiber probe was evaluated by Zemax analysis tool and theoretical model. The performance of the system was verified by experiments. The experimental results show that the measurement accuracy of axial clearance is better than 10.5 um within the range of 0.5-20.5 mm. The measurement accuracy has been effectively improved compared to previous methods. Additionally, the probe size is reduced to a mere diameter of 2.78 mm, which is more suitable for axial clearance measurement in narrow spaces inside rotating machines.

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.

Influence of Estrus on Dairy Cow Milk Exosomal miRNAs and Their Role in Hormone Secretion by Granulosa Cells.

Estrus is crucial for cow fertility in modern dairy farms, but almost 50% of cows do not show the behavioral signs of estrus due to silent estrus and lack of suitable and high-accuracy methods to detect estrus. MiRNA and exosomes play essential roles in reproductive function and may be developed as novel biomarkers in estrus detection. Thus, we analyzed the miRNA expression patterns in milk exosomes during estrus and the effect of milk exosomes on hormone secretion in cultured bovine granulosa cells in vitro. We found that the number of exosomes and the exosome protein concentration in estrous cow milk were significantly lower than in non-estrous cow milk. Moreover, 133 differentially expressed exosomal miRNAs were identified in estrous cow milk vs. non-estrous cow milk. Functional enrichment analyses indicated that exosomal miRNAs were involved in reproduction and hormone-synthesis-related pathways, such as cholesterol metabolism, FoxO signaling pathway, Hippo signaling pathway, mTOR signaling pathway, steroid hormone biosynthesis, Wnt signaling pathway and GnRH signaling pathway. Consistent with the enrichment signaling pathways, exosomes derived from estrous and non-estrous cow milk both could promote the secretion of estradiol and progesterone in cultured bovine granulosa cells. Furthermore, genes related to hormonal synthesis (CYP19A1, CYP11A1, HSD3B1 and RUNX2) were up-regulated after exosome treatment, while exosomes inhibited the expression of StAR. Moreover, estrous and non-estrous cow-milk-derived exosomes both could increase the expression of bcl2 and decrease the expression of p53, and did not influence the expression of caspase-3. To our knowledge, this is the first study to investigate exosomal miRNA expression patterns during dairy cow estrus and the role of exosomes in hormone secretion by bovine granulosa cells. Our findings provide a theoretical basis for further investigating milk-derived exosomes and exosomal miRNA effects on ovary function and reproduction. Moreover, bovine milk exosomes may have effects on the ovaries of human consumers of pasteurized cow milk. These differential miRNAs might provide candidate biomarkers for the diagnosis of dairy cow estrus and will assist in developing new therapeutic targets for cow infertility.

How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

High accuracy compact difference and multigrid methods for two-dimensional time-dependent nonlinear advection-diffusion-reaction problems

We focus on high-accuracy and stable numerical methods for the time-dependent nonlinear advection-diffusion-reaction problems in this work. A novel fourth-order fully implicit compact difference scheme is derived, in which we make use of the fourth-order compact formulas to discretize the diffusion terms, the fourth-order Padé formulas to compute the nonlinear advection terms, and the fourth-order backward differencing formula to approximate the time derivative term. Convergence and stability of the new scheme are analysed by the energy method. On the basis of the novel scheme, a multigrid algorithm is established such that a time advancement algorithm for solving these nonlinear problems are obtained. We provide various numerical experiments to verify the performances of the proposed scheme including the reliability, stability and computational efficiency. And the results obtained by our method show it is more accurate than most same kind schemes reported in the literature.

Revealing the properties of the out-of-plane points of equilibrium of the restricted 3-body problem with non-spherical radiating bodies

Our target is to understand and also reveal the dynamics of the out-of-plane points of equilibrium of the restricted version of the 3-body problem with equally massed radiating bodies that have either prolate or oblate shapes. We use standard root-finding numerical methods of high accuracy for determining the coordinates of the locations of the out-of-plane equilibria and also for computing their linear stability. For obtaining a global view of how the two system’s parameters, that is the prolateness/oblateness coefficient and the radiation pressure factor, affect the equilibria dynamics we perform a systematic and thorough scan of the parameters space. Our analysis suggests that the out-of-plane equilibria are highly affected by these two parameters and play a key role in the overall dynamics of the system.

Hardware-efficient algorithm and architecture design with memory and complexity reduction for semi-global matching

Among the stereo matching algorithms, the semi-global matching (SGM) is an efficient and high-accuracy method. However, its huge demand for memory access and high computational complexity makes it difficult to achieve a real-time and efficient processing on hardware. Based on the spatial redundancy found in the matching cost, we propose some effective techniques to reduce the requirement of on-chip and off-chip memory, while simultaneously greatly lower the computational complexity. Experimental results present that the proposed SGM algorithm reduces the computational complexity by 71%–74% and has almost the same quality of disparity map compared with the original 8-path SGM. The proposed 3-path fully-pipelined architecture is implemented on the Xilinx VCU-106 with a throughput of 1920 × 1080/54 fps. We also synthesize and layout it with TSMC 40 nm standard library, leading to an area of 8.1 mm2 with throughput of 1920 × 1080/192 fps. The million disparity estimation per second (MDE/s) of the proposed design reaches up to 50,960, which outperforms conventional ASIC implementations.

Qualitative and Quantitative Assessments of Apple Quality Using Vis Spectroscopy Combined with Improved Particle-Swarm-Optimized Neural Networks.

Exploring a cost-effective and high-accuracy optical detection method is of great significance in promoting fruit quality evaluation and grading sales. Apples are one of the most widely economic fruits, and a qualitative and quantitative assessment of apple quality based on soluble solid content (SSC) was investigated via visible (Vis) spectroscopy in this study. Six pretreatment methods and principal component analysis (PCA) were utilized to enhance the collected spectra. The qualitative assessment of apple SSC was performed using a back-propagation neural network (BPNN) combined with second-order derivative (SD) and Savitzky-Golay (SG) smoothing. The SD-SG-PCA-BPNN model's classification accuracy was 87.88%. To improve accuracy and convergence speed, a dynamic learning rate nonlinear decay (DLRND) strategy was coupled with the model. After that, particle swarm optimization (PSO) was employed to optimize the model. The classification accuracy was 100% for testing apples via the SD-SG-PCA-PSO-BPNN model combined with a Gaussian DLRND strategy. Then, quantitative assessments of apple SSC values were performed. The correlation coefficient (r) and root-square-mean error for prediction (RMSEP) in testing apples were 0.998 and 0.112 °Brix, surpassing a commercial fructose meter. The results demonstrate that Vis spectroscopy combined with the proposed synthetic model has significant value in qualitative and quantitative assessments of apple quality.

BUILDING COMPONENT DEFORMATION TRACKING USING STRUCTURED LIGHT TECHNIQUE

Abstract. It is crucial to fully master the mechanical properties of building components for ensuring the safety of infrastructures during service. The laboratory deformation test is an important way to study the mechanical properties of building components. At present, there is still a lack of a combination of high-efficiency, high-accuracy, and low-cost measurement methods for laboratory deformation tests. To address the issue, a method is proposed to measure the deformation of building components by using the structured light technique in this paper. It is applied to the axial tensile test of the dog-bone specimen, and the 3D full-field displacement of the dog-bone specimen is obtained by calculating the established deformation tracking model. The experimental results demonstrate the deformation measurement accuracy is at the millimeter level and can meet the accuracy requirement in the laboratory. Furthermore, the results are consistent with the way of force and the deformation mechanism of the dog-bone specimen. This paper presents a novel method for the deformation measurement, which provides a reliable analysis basis for studying the mechanical properties of building components.

A Lightweight Attentional Shift Graph Convolutional Network for Skeleton-Based Action Recognition

In the field of skeleton-based human behavior recognition, graph convolutional neural networks have made remarkable achievements. However, high precision networks are often accompanied by numerous parameters and computational cost, and their application in mobile devices has considerable limitations. Aiming at the problem of excessive spatiotemporal complexity of high-accuracy methods, this paper further analyzes the lightweight human action recognition model and proposes a lightweight architecture attentional shift graph convolutional network. There are three main improvements in this model. Firstly, shift convolution is a lightweight convolution method that can be combined with graph convolution to effectively reduce its complexity. At the same time, a shallow architecture for multi-stream early fusion is designed to reduce the network scale by merging multi-stream networks and reducing the number of network layers. In addition, the efficient channel attention module is introduced into the model to capture the underlying characteristic information in the channel domain. Experiments are conducted on the three existing skeleton datasets, NTU RGB+D, NTU-120 RGB+D, and Northwestern-UCLA. Results demonstrate that the proposed model is not only competitive in accuracy, but also outperforms current mainstream methods in parameter count and computational cost, and supports running in some devices with limited computing and storage resources.