Modeling Scheme Research Articles

Design of a Hierarchical-Type Control System Based on Smart MBD Approach and its Application to Hydraulic Excavator

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

When wavelet decomposition meets external attention: a lightweight cloud server load prediction model

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.

A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics

As an indispensable component of air-conditioning system, the air handling unit (AHU) always exhibits strong nonlinearity and two-directional dynamics (time-wise and batch-wise dynamics). However, existing monitoring strategies cannot adequately figure out the AHU's two-directional dynamics from the nonlinear data. To boost the AHU's fault detection performance, a novel global modelling scheme integrated dynamic kernel canonical variate analysis (GDKCVA) is developed in this paper. The first contribution is to collect datasets from various days to develop a three-way training dataset and then normalize it along the batch-wise to suppress the batch-wise dynamics and variables' correlations. The second contribution is to establish the dynamic kernel CVA by integrating autoregressive moving average exogenous framework to more effectively confront the time-wise dynamics and variables' nonlinear relationships. The kernel matrices of augmented batch dataset are determined utilizing the kernel trick that transforms nonlinear data into high-dimensional linear space by leveraging kernel function. The third contribution is to propose a novel global modelling strategy to further eliminate the batch-wise dynamics by computing the total average kernel matrices from all the normal batch datasets. Finally, experiments and comparisons on the ASHRAE RP-1312 datasets are carried out to assess the suggested GDKCVA's fault detection capability. Compared with the kernel CVA (KCVA), the three-way training dataset based CVA (TCVA) and the three-way training dataset based KCVA (TKCVA), the proposed GDKCVA has the highest fault detection rates and its average fault detection rates of three monitoring statistics for eleven fault patterns reach 99.38 %, 99.15 % and 100 %, respectively.

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation.

In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter.

Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation

Mobile observation has been widely used in the monitoring of air pollution. However, studies on pollution sources and emission characteristics based on mobile navigational observation are rarely reported in the literature. A method for quantitative source analysis for industrial air pollutant emissions based on mobile observations is introduced in this paper. NOx pollution identified in mobile observations is used as an example of the development of the method. A dispersion modeling scheme that fine-tuned the meteorological parameters according to the actual meteorological conditions was adopted to minimize the impact of uncertainties in meteorological conditions on the accuracy of small-scale dispersion modeling. The matching degree between simulated and observed concentrations was effectively improved through this optimization search. In response to the efficiency requirements of source resolution for multiple sources, a random search algorithm was first used to generate candidate solution samples, and then the solution samples were evaluated and optimized. Meanwhile, the new index Smatch was established to evaluate the quality of candidate samples, considering both numerical error and spatial distribution error of concentration, in order to address the non-uniqueness of the solution in the multi-source problem. Then, the necessity of considering the spatial distribution error of concentration is analyzed with the case study. The average values of NOx emission rates for the two study cases were calculated as 69.8 g/s and 70.8 g/s. The Smatch scores were 0.92–0.97 and 0.92–0.99. The results were close to the online monitoring data, and this kind of pollutant emission monitoring based on the mobile observation experiment was initially considered feasible. Additional analysis and clarifications were provided in the discussion section on the impact of uncertainties in meteorological conditions, the establishment of a priori emission inventories, and the interpretation of inverse calculation results.

Unconditionally energy stable high-order BDF schemes for the molecular beam epitaxial model without slope selection

In this paper, we consider a class of k-order (3≤k≤5) backward differentiation formulas (BDF-k) for the molecular beam epitaxial (MBE) model without slope selection. Convex splitting technique along with k-th order Douglas-Dupont regularization term τnk(−Δ)kD_kϕn (D_k represents a truncated BDF-k formula) is added to the numerical schemes to ensure unconditional energy stability. The stabilized convex splitting BDF-k (3≤k≤5) methods are unique solvable unconditionally. Then the modified discrete energy dissipation laws are established by using the discrete gradient structures of BDF-k (3≤k≤5) formulas and processing k-th order explicit extrapolations of the concave term. In addition, based on the discrete energy technique, the L2 norm stability and convergence of the stabilized BDF-k (3≤k≤5) schemes are obtained by means of the discrete orthogonal convolution kernels and the convolution type Young inequalities. Numerical results are carried out to verify our theory and illustrate the validity of the proposed schemes.

Extending our understanding on the retrievals of surface energy fluxes and surface soil moisture from the “triangle” technique

The present study demonstrates the capability of an inversion modelling scheme so-called the “triangle” to retrieve spatiotemporal estimates of surface energy fluxes and soil surface moisture (SSM) at high resolution using ASTER satellite imagery synergistically with SimSphere land biosphere model. In addition, as a further objective of this study is to examine the use of the technique for retrieving the Evaporative (EF) and the Non-Evaporative (NEF) Fractions as representations of the daytime average fluxes. The applicability of the investigated technique, is demonstrated for sixteen calendar days of year 2011 using in-situ data acquired from nine CarboEurope sites representing a variety of climatic, topographic and environmental conditions. Results indicated a close agreement between all the inverted parameters and the corresponding in-situ data. SSM predicted maps showed a small bias of 0.08 vol vol−1, a scatter of 0.18 vol vol−1 and a RMSD of 0.19 vol vol−1. The predicted LE fluxes showed a relatively low overall agreement (RMSD = 65.10 Wm-2), whereas for H flux reported RMSD was 85.02 Wm-2. The results also confirmed the ability of the investigated technique to provide meaningful estimates of the NEF and EF. All in all, the present study findings were at least comparable, or of higher accuracy, to those reported in other similar verification studies of the “triangle” using both high resolution (airborne) and low resolution (satellite) data. To our knowledge, this study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting combining the SimSphere SVAT model and ASTER EO datasets.

Prediction-based multi-objective optimization method for 3D printing resource consumption

A prediction-based multi-objective optimization (PBMO) method is proposed in this paper to forecast and reduce 3D printing (3DP) resources on demand, including time, energy, and material. In the authors’ previous research work, a hybrid code-based and data-driven modeling (HCDM) scheme was proposed to customize the predictive models based on process parameters, material deposition paths, and machine behaviors. This study further utilizes the models as multi-objectives to be minimized, aiming at the appropriate solution of process parameters that consume the least resources. Non-dominated sorting genetic algorithm II (NSGA-II), one of the commonly used metaheuristic algorithms, is adopted to construct the PBMO framework, where the HCDM process is embedded in the fitness evaluation step. The corresponding computing program is compiled and then validated on two material extrusion (MEX) machines. Based on the optimization results, hypervolume, as a Lebesgue measure, is used to evaluate the superiorities of all near-optimal solutions, thereby recommending the best-performing solutions for real 3DP. Apart from the 3DP process, the proposed optimization method is adaptable to other mainstream computer numerical control (CNC) manufacturing processes and will guide process design to promote resource conservation for cleaner production.

AI hybrid survival assessment for advanced heart failure patients with renal dysfunction

Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system’s robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com, empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

A convergent finite-volume scheme for the diffusive compressible Euler model—resolved and under-resolved flows

The purpose of this paper is to demonstrate the robustness and accuracy of a provably convergent finite-volume scheme for the diffusive compressible Euler model. To this end, we consider sub- and supersonic flows as well as well-resolved smooth flows and under-resolved turbulent flows. The scheme is formulated for Voronoi meshes to allow complex geometries. It is completely defined without any parameters that require case-by-case tuning. Provable convergence guarantees both stability and admissibility of the numerical solutions, which is confirmed in our computations for both strong shocks and turbulence. For the turbulent Taylor-Green vortex, the scheme performs as well as a linearly-stable minimally-diffusive second-order scheme and for vortex shedding behind a cylinder, it even outperforms energy-stable second-order schemes.

Comparison of Two-Moment and Three-Moment Bulk Microphysics Schemes in Thunderstorm Simulations over Indian Subcontinent

We simulated three different thunderstorm events over India using a regional model with two different multi-moments cloud microphysical schemes: (i) Morrison double moment (Morrison-2 M) and (ii) National Taiwan University (NTU) triple moment (NTU-3 M). The results show that the choice of microphysical scheme significantly impacts storm structures, cloud formations, lightning intensity, and rainfall patterns. The lightning flash counts from the in-situ lightning detection network (LDN) are also used to compare the simulated storms. The Lightning Potential Index (LPI) was computed for both Morrison-2 M and NTU-3 M microphysics schemes and compared it with the LDN observation. Differences in simulations are attributed to the simulation of ice crystals, snow, and graupel by the microphysical schemes. The effects on the size distributions of cloud hydrometeors between the two microphysical schemes are also found to be important. The inclusions of ice crystal shapes likely accounted for the key differences between the two microphysics schemes. The results demonstrate that the simulation of lightning event characteristics is sensitive to the choice of microphysical parameterization schemes in numerical weather prediction models. In comparison to both the microphysical schemes, some aspects are better in the 2 M microphysics scheme, and some are better in the 3 M microphysics scheme. Morrison-2 M simulates more mixing ratio for the majority of cloud hydrometeors across all cases than the NTU-3 M whereas the Morrison-2 M has less rainfall bias over the ocean for the majority of the cases. Overall, the present study suggests that the choice of microphysical scheme can greatly influence the accuracy of thunderstorm and lightning event predictions in operational weather forecasting models.

Convergence analysis of an efficient scheme for the steady state second grade fluid model

We are interested in studying the stationary second grade fluid model in a bounded domain in R2. To approximate the solution of the continuous model, we propose a fully decoupled numerical scheme based on a splitting method combined with the use of the Grad–Div operator. This approach allows the complete decoupling of the three variables: velocity, pressure and vorticity. Each variable is computed using an iterative procedure, with the pressure step involving a simple L2-projection.We provide a proof of the convergence of the scheme to the continuous problem under smallness assumptions on the data. This theoretical analysis ensures the reliability of our method in approximating the behavior of the stationary second grade fluid model.Finally, we present several numerical tests to validate our approach. These tests illustrate the effectiveness and efficiency of our scheme in various scenarios, highlighting its potential applicability to a wide range of problems involving second grade fluids.

Rigid–Flexible Coupled Modeling and Flexoelectric Control for Flexible Rotating Structures

The pursuit of lightweight structures has propelled spacecraft with large flexible appendages to the forefront of modern aerospace engineering. Flexoelectric actuators, with their inherent self-sensing and self-actuating capabilities, perfectly align with the demand for lightweight structures. This paper presents a dynamic model and a flexoelectric vibration control method tailored for spacecraft equipped with single-sided flexible appendages. The spacecraft system is a central rigid hub with flexible cantilever beam appendages that allow for single-axis rotation. The flexible beam has a flexoelectric element attached to it. Leveraging Hamilton’s principle, the equations of motion are derived, accounting for flexoelectric effects, the centrifugal stiffening effect, and the coupling between rigid body attitude and flexible beam vibration. A closed-loop proportional–derivative controller is crafted for attitude control of the system. Additionally, a flexoelectric patch is employed as an actuator for active vibration control of the flexible beam. The precision and efficacy of the proposed model and control scheme are meticulously validated through numerical simulation. The results indicate that the flexoelectric patch can effectively manage the vibration of the flexible beam, consequently impacting the attitude control performance of the system. Furthermore, the flexoelectric effect of the system under static conditions is scrutinized, and the influence of the flexoelectric actuator on the rigid–flexible coupling characteristics of the system is thoroughly analyzed. These analyses not only confirm the feasibility of flexoelectricity in rigid–flexible coupled systems, but also open avenues for applications and optimizations in intelligent control methods for flexible appendages.

A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding.

Video compression is indispensable to most video analysis systems. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i.e., task-decoupled, label-free, and data-emerged semantic prior, are critical to a machine-friendly coding framework but are not fully satisfied so far. In this paper, we propose a traditional-neural mixed coding framework that simultaneously fulfills all these principles, by taking advantage of both traditional codecs and neural networks (NNs). On one hand, the traditional codecs can efficiently encode the pixel signal of videos but may distort the semantic information. On the other hand, highly non-linear NNs are proficient in condensing video semantics into a compact representation. The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved w.r.t. the coding procedure, which is spontaneously learned from unlabeled data in a self-supervised manner. The videos collaboratively decoded from two streams (codec and NN) are of rich semantics, as well as visually photo-realistic, empirically boosting several mainstream downstream video analysis task performances without any post-adaptation procedure. Furthermore, by introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of our approach is further enhanced. Fianlly, we build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach. All codes, data, and models will be open-sourced for facilitating future research.

Exploring the similarity relationships from the nondimensionalization of atmospheric turbulence

Nondimensionalization, a theoretical approach for establishing interconnections among parameters within a set of equations, has proven to be an effective tool for the analysis of atmospheric turbulence. By applying nondimensionalization to turbulence equations, a concise form of dimensionless turbulence functions can be obtained. This process also yields several dimensionless parameters, defined as combinations of characteristic scales. From the dimensionless tensor B\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${B}$$\\end{document} and vector βθ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\varvec{\\beta}}}_{\ heta}$$\\end{document} introduced in this study, the characteristic length scale, zs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${z}^{s}$$\\end{document}, can be defined as an alternative of length scale in similarity theories. Using the data from observational station in Horqin Sandy Land, quantified verifications of similarity relationships are carried out. The dimensionless parameters derived from nondimensionalization is not only in accordance with traditional turbulence theories but also facilitate the derivation of relationships among other dimensionless parameters. This reveals new similarity relationships that supplement the Monin–Obukhov theory. Under conditions of flat terrain and steady motions, the new length scale gives rise to similarity relationships exhibiting “4/3” exponential and near-linear patterns, which are associated with turbulent transport. These results make it possible to obtain the turbulent fluxes directly from the statistics of meteorological elements, even in stable stratifications. Consequently, the method of nondimensionalization can be taken as a reference in parameterization schemes of turbulence and climate models, and is fruitful in prospect of further study on atmospheric turbulence.

A class of efficient high-order time-stepping methods for the anisotropic phase-field dendritic crystal growth model

In this paper, we develop and analyze a novel class of arbitrarily high-order and unconditionally energy stable schemes for the anisotropic phase-field dendritic crystal growth model, which is a highly nonlinear system that combines the anisotropic Allen–Cahn equation with the thermal equation. The proposed schemes are based on an extrapolated and linearized Runge–Kutta method for an auxiliary variable reformulation of the crystal growth model. A delicate implementation demonstrates that the proposed method can be realized in a very efficient way, requiring only the solution of a coupled linear elliptic system at each time step. We prove that the constructed schemes satisfy the energy dissipation property and demonstrate the convergence order through a consistency error analysis. To the best of our knowledge, this is the first unconditionally stable scheme of arbitrarily high-order for the anisotropic phase-field dendritic crystal growth model. Numerical experiments for two and three dimensional dendritic crystal growth are simulated to verify our theoretical accuracy as well as the efficiency of the proposed method. Furthermore, to emphasize the superiority of the high-order schemes, we present a numerical comparison that directly contrasts its performance with the lower-order schemes.

Wheel Wear Modeling and Re-Profiling Strategy Optimization Based on the Maintenance Data of a Three-Axle Bogie Locomotive

Wear caused by wheel–rail contact forces is inevitable during vehicle operation, which has an important impact on the security and stability of train operation. Therefore, it is of great significance to study wheel wear patterns and optimize re-profiling strategies to extend service life. Based on the wheel wear data of three-axle bogie locomotives, this paper proposes a data-driven hybrid wheel wear model and optimization schemes of the re-profiling strategy. The wear model consists of a wheel flange thickness wear model, a wheel diameter wear model, and a re-profiling ratio coefficient model. Then, utilizing the above models, the optimization of the re-profiling strategy for different axle position wheels is raised, and the optimization of the complete vehicle re-profiling strategy is presented by considering the wheel diameter difference. Finally, a wheel data-driven analysis platform was developed to enable the management and utilization of wheel maintenance data. Analysis of extensive maintenance data indicates that the guide wheels wear the fastest, approximately 22.2% higher than the middle wheels, which wear the slowest. The re-profiling ratio coefficient model indicates that the ratio coefficient increases as the wheel flange thickness before re-profiling increases. Simulations demonstrate a longer expected wheel life with a flange thickness between 28 and 32.5 mm. Compared to measured values, the optimization strategy reduces complete vehicle re-profiling by 21.5%. Through the initial implementation at CRRC Dalian Locomotive Ltd, it has become evident that this methodology offers a viable solution to enhance the service longevity of locomotive wheels.

Implementation of a slope stability method in the CRITERIA-1D agro-hydrological modeling scheme

AbstractThis paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model

Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods have low accuracy in extracting geoscientific dataset names from unstructured text because geoscientific dataset names are a complex combination of multiple elements, such as geospatial coverage, temporal coverage, scale or resolution, theme content, and version. This paper proposes a new method based on the hierarchical temporal memory (HTM) model, a brain-inspired neural network with superior performance in high-level cognitive tasks, to accurately extract geoscientific dataset names from unstructured text. First, a word-encoding method based on the Unicode values of characters for the HTM model was proposed. Then, over 12,000 dataset names were collected from geoscience data-sharing websites and encoded into binary vectors to train the HTM model. We conceived a new classifier scheme for the HTM model that decodes the predictive vector for the encoder of the next word so that the similarity of the encoders of the predictive next word and the real next word can be computed. If the similarity is greater than a specified threshold, the real next word can be regarded as part of the name, and a successive word set forms the full geoscientific dataset name. We used the trained HTM model to extract geoscientific dataset names from 100 papers. Our method achieved an F1-score of 0.727, outperforming the GPT-4- and Claude-3-based few-shot learning (FSL) method, with F1-scores of 0.698 and 0.72, respectively.

An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.