Space Model Research Articles

Real-time estimation of fuel injection rate and injection volume in high-pressure common rail systems

The injection rate and injection volume are essential parameters for the combustion process, which greatly affect the engine efficiency and emission performance. However, at the current time the injection information cannot be obtained in real time during the actual operation of engine. This paper presents a novel real-time estimation method for the injection rate and injection volume based on the measurable rail pressure. A comprehensive dynamic model is derived by considering both the effect of fuel injection and supply process on the pressure fluctuation. Then a linear time-varying state space model is constructed by choosing three appropriate state variables. On this basis, considering the measurement noise and model uncertainty, a Kalman filter-based estimation method of the injection information is investigated. And the noise covariance matrix Q is optimally designed to adapt to a wide range of operating conditions. The proposed estimation method is validated on the different conditions, and the results show the injection rate estimation errors are within 4.5 %, the injection volume estimation errors are within 4 %. And on the situation with overlap of injection and supply process, the injection rate and injection volume estimation errors are all within 4.5 %.

Implementation of the Vector Space Model Algorithm for Document Searching in Material Test Monitoring Applications (Case Study: BPJN Jayapura Lab)

The material test monitoring application built in 2021 aims to facilitate BPJN Jayapura's work in entering administrative data files to facilitate the material test monitoring process carried out from the process stage to the material test results stage. However, in using it for 2 years, the application has had problems. The application is unable to search for documents stored in the database so the search is carried out one by one. This makes it difficult for users because there are more than 30 documents. Therefore, this research will focus on creating a document search engine (search engine) using the Vector Space Model method. This algorithm will measure the level of similarity of documents to the keywords or queries entered by the user. This research implements the Vector Space Model method to make it easier to search for documents in the application. In carrying out this research, the Software Development Life Cycle methodology will be used as a method for creating the search engine using the Unified Modeling Language version 2.5.1 modeling tool and the PHP programming language. Based on the tests that have been carried out, the Vector Space Model method has been successfully implemented and is able to function as a search engine with a precision level of 0.83 and a recall of 1 for searching 40 documents as samples. Keywords: Document search; Jayapura; vector space model

Geodesics and magnetic curves in the 4-dim almost Kähler model space F4

Abstract We study geodesics and magnetic trajectories in the model space F 4 {{\rm{F}}}^{4} . The space F 4 {{\rm{F}}}^{4} is isometric to the 4-dim simply connected Riemannian 3-symmetric space due to Kowalski. We describe the solvable Lie group model of F 4 {{\rm{F}}}^{4} and investigate its curvature properties. We introduce the symplectic pair of two Kähler forms on F 4 {{\rm{F}}}^{4} . Those symplectic forms induce invariant Kähler structure and invariant strictly almost Kähler structure on F 4 {{\rm{F}}}^{4} . We explore some typical submanifolds of F 4 {{\rm{F}}}^{4} . Next, we explore the general properties of magnetic trajectories in an almost Kähler 4-manifold and characterize Kähler magnetic curves with respect to the symplectic pair of Kähler forms. Finally, we study homogeneous geodesics and homogeneous magnetic curves in F 4 {{\rm{F}}}^{4} .

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. The proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10−7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10−5. The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

Multienergy load forecasting model for integrated energy systems based on coupling auxiliary sequences and multitask learning

AbstractMultienergy load forecasting (MELF) with high accuracy is crucial for the economic operation and optimal dispatch of the integrated energy system (IES). Within such systems, electrical, heat, and cold loads may exhibit complex and highly coupled relationships. The accuracy of MELF can be improved by exploiting the coupling of multienergy loads. To address this issue, the authors propose a novel framework named CAFormer (coupling auxiliary transformer) that leverages coupling auxiliary forecasting and multitask learning (MTL) to improve MELF in IES. CAFormer adopts an auxiliary forecasting coupling strategy. The aim is to construct coupling auxiliary sequences by projecting different sequences onto the same coupling space to capture the interdependencies between them. The model space structure is more complex when incorporating the coupling space. Furthermore, the authors’ framework deviates from the traditional approach of treating the prediction task of multiple sequences as multiple tasks by integrating the contrastive learning task and the prediction task into a single multitask paradigm. This avoids the issue of sequence entanglement that arises when different loads directly share information in the traditional multitask prediction of multiple energy loads. MTL based on coupling space similarity helps to learn the coupling relationships between sequences while maintaining the original sequence characteristics and refining the generation of coupling auxiliary sequences. The experimental results demonstrate that the authors’ proposed model can thoroughly explore the coupling relationships among energy systems and exhibit higher prediction accuracy and better prediction applicability than those of state‐of‐the‐art models.

Collective behavior from surprise minimization

Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from directed movement to milling to disordered swarming. Typically, such macroscopic patterns arise from decentralized, local interactions among constituent components (e.g., individual fish in a school). Preeminent models of this process describe individuals as self-propelled particles, subject to self-generated motion and "social forces" such as short-range repulsion and long-range attraction or alignment. However, organisms are not particles; they are probabilistic decision-makers. Here, we introduce an approach to modeling collective behavior based on active inference. This cognitive framework casts behavior as the consequence of a single imperative: to minimize surprise. We demonstrate that many empirically observed collective phenomena, including cohesion, milling, and directed motion, emerge naturally when considering behavior as driven by active Bayesian inference-without explicitly building behavioral rules or goals into individual agents. Furthermore, we show that active inference can recover and generalize the classical notion of social forces as agents attempt to suppress prediction errors that conflict with their expectations. By exploring the parameter space of the belief-based model, we reveal nontrivial relationships between the individual beliefs and group properties like polarization and the tendency to visit different collective states. We also explore how individual beliefs about uncertainty determine collective decision-making accuracy. Finally, we show how agents can update their generative model over time, resulting in groups that are collectively more sensitive to external fluctuations and encode information more robustly.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Global differentiable structures for the Fisher-Rao and Kantorovich-Wasserstein-Otto metrics

We develop a class of non-parametric, Banach-Sobolev manifolds of probability measures that, despite having comparatively weak topologies, support the Fisher-Rao and Kantorovich-Wasserstein-Otto (KWO) Riemannian metrics. The manifolds employ the Kaniadakis κ-deformed logarithms in their charts, and are isomorphic to the (whole) model spaces, W1,λ(μ). These are weighted Sobolev spaces with Lebesgue exponents as small as λ=4, making them suitable for approximations. The KWO metric is analysed through a bundle of Markov semigroups having generators with irregular first-order terms. Strong a-priori estimates are obtained for the associated parabolic equations. Together with a weak Poincaré/Lp-convergence theorem, these are used to establish the Hölder continuity of the “velocity field” representation of tangent vectors—a property inherited by the KWO metric. The manifolds provide a simple framework for the study of problems involving both metrics.

Determining a musculoskeletal system’s pre-stretched state using continuum–mechanical forward modelling and joint range optimization

The subject-specific range of motion (RoM) of a musculoskeletal joint system is balanced by pre-tension levels of individual muscles, which affects their contraction capability. Such an inherent pre-tension or pre-stretch of muscles is not measureable with in vivo experiments. Using a 3D continuum mechanical forward simulation approach for motion analysis of the musculoskeletal system of the forearm with 3 flexor and 2 extensor muscles, we developed an optimization process to determine the muscle fibre pre-stretches for an initial arm position, which is given human dataset. We used RoM values of a healthy person to balance the motion in extension and flexion. The performed sensitivity study shows that the fibre pre-stretches of the m. brachialis, m. biceps brachii and m. triceps brachii with 91%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$91\\%$$\\end{document} dominate the objective flexion ratio, while m. brachiradialis and m. anconeus amount 7.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.8\\%$$\\end{document} and 1.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.2\\%$$\\end{document}. Within the multi-dimensional space of the surrogate model, 3D sub-spaces of primary variables, namely the dominant muscles and the global objective, flexion ratio, exhibit a path of optimal solutions. Within this optimal path, the muscle fibre pre-stretch of two flexors demonstrate a negative correlation, while, in contrast, the primary extensor, m. triceps brachii correlates positively to each of the flexors. Comparing the global optimum with four other designs along the optimal path, we saw large deviations, e.g., up to 15∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} in motion and up to 40% in muscle force. This underlines the importance of accurate determination of fibre pre-stretch in muscles, especially, their role in pathological muscular disorders and surgical applications such as free muscle or tendon transfer.

Order selection for heterogeneous semiparametric hidden Markov models.

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

On n-Dimensional Maxwell and Dirac Equations in Curved Space-Time and Its Applications in SO(P,Q) Group Theoretic Image Processing

Maxwell equations in p time and q spatial dimensions are formulated. Properties of the Green’s function for the associated (p,q)-wave operator are derived based on contour integration. The notions of electric and magnetic fields in (p,q)-dimensions is explored by analogy with four dimensional physics and the problem of far field computation of the radiation field generated by charges and currents in \((1,n-1)\) space-time is analyzed. SO(p,q) invariance properties of the Maxwell equations are deduced and used to formulate SO(p,q)-group theoretic image processing problems in (p,q)-dimensions. Dirac’s equation in (p,q)-dimensional space-time is derived using the Clifford algebra of the Dirac gamma matrices. SO(p,q)-invariance of the Dirac equation based on the spinor representation of \(SO(p,q)\) is mentioned. The Maxwell’s equations in (p,q)-dimensional curved space-time is analyzed using perturbation theory in the context of maximally symmetric spaces which play a fundamental role in \((p,q)\)-dimensional cosmological models for homogeneous and isotropic spaces. The Einstein-Maxwell equations for (p,q)-dimensional gravity and electromagnetism is studied and used to derive the equations of motion of point charges carrying mass moving under mutual gravitational and electromagnetic interactions in general relativity. Finally, Dirac’s equation in (p,q)-dimensional curved space-time interacting with the (p,q)-dimensional electromagnetic field is looked at. \(U(1)\)-Gauge, local SO(p,q) Lorentz and diffeomorphism invariance of this equation is analyzed. Local \(SO(p,q)\) invariance of the curved space-time Dirac equation is deduced based on transformation properties of the Dirac matrices under the spinor representation. The appendix presents some applications of group representation theoretic statistical image processing on a manifold to the situation when the image field is described by the six component electromagnetic field tensor on which the Lorentz group acts.

Comparing combinatorial models of moduli space and their compactifications

Comparing combinatorial models of moduli space and their compactifications

A multimodal shared network with a cross-modal distribution constraint for continuous emotion recognition

Continuous emotion recognition has been a compelling topic in affective computing because it can interpret human emotions subtly and continuously. Existing studies have achieved advanced emotion recognition performance using multimodal knowledge. However, these studies generally ignore the circumstances where some particular modalities are missing in the inference phase and thus become sensitive to the absence of modalities. To resolve this issue, we propose a novel multimodal shared network with a cross-modal distribution constraint, i.e. the DS-Net, which aims to improve the robustness of the model to missing modalities. The training process of the proposed network generally includes two components: multimodal shared space modeling and a cross-modal distribution matching constraint. The former utilizes the local and temporal information of multimodal signals for multimodal shared space modeling, while the latter further enhances the multimodal shared space via a loose constraint method. Coupled with the latter, the former can effectively exploit the complementarity between videos and peripheral physiological signals (PPSs), thus enhancing the discriminative capability of the shared space. Based on the shared space, the DS-Net works during the inference phase with only one modality input and can leverage multimodal knowledge to improve emotion recognition accuracy. Comprehensive experiments were conducted on two public datasets. Results demonstrate that the proposed method is competitive or superior to the current state-of-the-art methods. Further experiments indicate that the proposed method can be extended to handle other modalities and to deal with partially missing modalities, demonstrating its potential in real-world applications.

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.

Frequency domain model-augmented feature space transferable attack

Deep neural networks (DNNs) are susceptible to adversarial attacks, and one important factor is that adversarial samples are transferable, i.e., adversarial samples generated by a particular network may deceive other black-box models. However, existing transferable adversarial attacks tend to modify the input features of images directly without selection to reduce the prediction accuracy in the alternative model, which would enable the adversarial samples to fall into the model’s local optimum. Alternative models differ significantly from the victim model in most cases, and while simultaneously attacking multiple models may improve transferability, gathering numerous different models is more challenging and expensive. We simulate various models using frequency domain transformation to close the gap between the source and victim models and improve transferability. At the same time, we destroy important intermediate layer features that influence the decision of the model in the feature space. Additionally, smoothing loss is introduced to remove high-frequency perturbations. Extensive experiments demonstrate that our FM-FSTA attack generates more well-hidden and transferable adversarial samples, and achieves a high deception rate even when attacking adversarially trained models. Compared to other methods, our FM-FSTA improved attack success rate under different defense mechanisms, which reveals the potential threats of current robust models.

An improved state space model predictive control for linear systems with input disturbance

This paper presents an improved model predictive control (MPC) algorithm for linear systems with input disturbance. Based on the developed extended non-minimum state space input disturbance (ENMSS-ID) model, the input disturbance model structure is incorporated into the MPC framework and the objective function of the MPC optimization problem is improved to weigh the system output increments. This enables the algorithm simultaneously to achieve good input disturbance rejection performance for systems with known input disturbances and reduce the controllers’ sensitivity to model mismatch. An existing optimal estimation method is introduced to estimate the input disturbance, together with the proposed strategy to improve estimation convergence. Offset-free property is also proven to show the steady-state performance of the designed control scheme. Finally, two benchmark plants are studied to illustrate the effectiveness and advantages of the proposed algorithm.

Were consumers of plant-based beef substitutes buyers of organic or grass-fed beef? Insights from correlations in willingness to pay

Organic and grass-fed beef serve as niche alternatives to conventionally grain-fed beef. A recent addition is plant-based beef substitutes, all three of which are marketed as environmentally friendly options. Despite this, it remains uncertain which specific products plant-based beef substitutes are most directly competing with. In this study, we investigate the correlations between the willingness to pay for plant-based beef substitutes and organic/grass-fed beef using a choice experiment and the Willingness to Pay Space model. Our findings indicate positive correlations between consumers' preferences for plant-based, organic, and grass-fed beef. This suggests that plant-based beef consumers are more likely to be prior consumers of organic or grass-fed beef. Given their relatively small market shares, novel beef substitutes could pose intense competition to the ecolabeled products.

Nonlinear control of electro‐hydraulic screw conveyor system for shield machine based on disturbance observer and back‐stepping method

AbstractIn order to improve the precision of earth pressure balance control and anti‐interference ability of shield sealing chamber, this paper proposes a nonlinear control strategy for the screw conveyor based on disturbance observer and back‐stepping method, so as to ensure the safe and efficient tunneling of the shield machine. According to the hydraulic flow dynamic balance principle of shield machine, the mechanism model of electro‐hydraulic screw conveyor system is established, and the system state space model is derived. The nonlinear controller of the screw conveyor is designed by using the inverse step method and the disturbance observer compensation characteristic, so that the system responds quickly and compensates for the flow disturbance and external force disturbance in real time. At last, the system stability is proven by using the Lyapunov function. The experimental results show that the method has high control accuracy with fast response and strong anti‐interference ability.

Potential-Energy Curves for the Ground and Several Electronic States of NdO and NdS.

Potential energy curves (PECs) were calculated for 21 and 18 electronic states of NdO and NdS molecules, respectively. In each case, static electron correlation effects were described by incomplete model space multiconfiguration self-consistent field wave functions based on an active space that included the most important valence orbitals. Dynamic electron correlation was included by the multireference second-order generalized Van Vleck perturbation theory method. Scalar-relativistic contributions were included by the effective core potential approach, using def2-TZVPP basis sets. Spin-dependent relativistic corrections were determined to be small and negligible for the Nd atom and so were not included in the calculations. The 21 and 18 electronic states of NdO and NdS were predicted to be in the excitation energy range of ∼3.2 and ∼2.7 eV, respectively. The ground electronic states of NdO and NdS were determined as 15H (6s4fσ4fϕ4fδ) and 15H (4fϕ4fπ4fπ6s), with spectroscopic constants: bond length Re = 1.780 and 2.325 Å, and harmonic frequency ωe = 891 and 538 cm-1, respectively.

Spectral relations for a matrix model in fermionic Fock space

Spectral relations for a matrix model in fermionic Fock space