Affine Model Research Articles

SINS/GNSS-Integrated Navigation Based on Group Affine SINS Mechanization in Local-Level Frame

In this article, a group affine strapdown inertial navigation system (SINS) mechanization in local-level frame is investigated for SINS and global navigation satellite system (GNSS) integrated navigation. Traditionally, the SINS mechanization in local-level frame is strong coupling and, therefore, is not group affine. In order to make it group affine, two main procedures are presented. One is making use of damping calculation for SINS, that is, the Coriolis and centrifugal force are ingeniously calculated outside the SINS calculation and filtering recursion using known velocity and position information provided by GNSS. The other is introducing an auxiliary velocity. Then, the trajectory-independent left-invariant error state-space model is derived corresponding to the group affine model. The derived group affine model and its corresponding error state-space model can be used in the SINS/GNSS indirect integration. Simulations and field tests are conducted to evaluate the actual performance of the investigated method. The results show that the proposed method has a much superior performance for SINS/GNSS with large initial attitude error. In contrast, the proposed method has a similar performance with existing state-of-the-art-integrated navigation method when the initial attitude error is small.

Projective transformations in metric-affine and Weylian geometries

We discuss generalizations of the notions of projective transformations acting on affine model of Riemann–Cartan and Riemann–Cartan–Weyl gravity which preserve the projective structure of the light-cones. We show how the invariance under some projective transformations can be used to recast a Riemann–Cartan–Weyl geometry either as a model in which the role of the Weyl gauge potential is played by the torsion vector, which we call torsion-gauging, or as a model with traditional Weyl (conformal) invariance.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

A Fast Gradient Iterative Affine Motion Estimation Algorithm Based on Edge Detection for Versatile Video Coding

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. The experimentalresults show that the encoding time of the affine motion estimation algorithm is about 15–35% lower than the overall encoding time of the anchor algorithm encoder, the average encoding time of the affine motion estimation part of the inter-frame prediction part is reduced by 24.79%, and the peak signal-to-noise ratio (PSNR) is only reduced by 0.04.

Evaluation of Technological Innovation Initiatives for Indian MSMEs Using the Fuzzy-Based Model

Micro, small and medium enterprises (MSMEs) are considered as a growing sector of the country due to their huge growth potential and possible social or economic effect, especially on employment and revenue generation. Existent globalised competition compels companies to constantly look for methods to improve their products and services. Without continuous innovation, manufacturing companies cannot remain competitive. Innovation represents the keyways the organisation responds to technical or market-related challenges. All manufacturers are currently trying to implement Multicriteria Decision-Making (MCDM) techniques such as Analytic Hierarchy Process, Piecewise Affine and fuzzy models, along with technology innovation initiatives (TIIs) for enhancing their production performance. In the current study, an attempt is made to assess the synergy fit of TIIs for MSMEs in India. The study considers metrics related to these initiatives, such as product performance and sales performance. In addition, these procedures use the MATLAB Fuzzy Logic Toolbox for the analysis of a Simulink block for designing and simulating the data set.

The incremental information in the yield curve about future interest rate risk

Using high-frequency intraday futures prices to measure yield volatility at selected maturities, we find that daily yield curves carry incremental information about future interest rate risk at the long end, relative to that contained in the time series of historical volatilities. Some of the information in the yield curves is not captured by standard affine models. Our results point to the existence of an unspanned stochastic volatility factor. Both time series and yield curve based forecasts provide utility to a risk averse investor, relative to a random walk. Information from the two sources can be combined to enhance yield volatility forecasting performance.

Experimental study and chemical affinity model on the inhibition of CO2 gas hydrate formation

Natural gas and water can form complex cage crystals in oil and gas pipelines under certain conditions. The natural gas hydrate formed after crystal clustering can block the pipeline and affect the operation efficiency of the pipeline, and even cause equipment damage, bringing economic and security problems to enterprises. In the present study, we experimentally investigated the inhibition of CO2 formation hydrate using attapulgite (ATP) and glucose (GLC). The kinetic influence on the formation of CO2 hydrate by the complex pairing of kinetic inhibitor ATP is conducted under the working conditions of 277.15 K and 3.5 MPa. The effect of compound inhibitors on the temperature and pressure conditions, growth process and gas consumption of hydrate formation are revealed. The chemical affinity model of CO2 hydrate formation under a compound inhibitor system is derived and established based on experimental studies. The results show that the combination of GLC and ATP can inhibit the formation of CO2 hydrate to varying degrees, prolong the induction time of hydrate, and reduce the consumption of CO2. The optimized experimental studies demonstrate that the best inhibitory compound system is 15 mg/mL GLC + 1.00 mg/mL ATP. Compared with the pure water system and single 15 mg/mL GLC system, the induction time is extended by 122.61% and 122.23%, while the gas consumption is reduced by 23.72% and 3.41%. The results provide new ideas and methods for the prediction of hydrate formation in the compound inhibitor system for the safe operation of oil and gas pipelines.

New Results on Nonsynchronous-Observer-Based Output-Feedback Control of Fuzzy-Affine-Model-Based Discrete-Time Nonlinear Systems

This work investigates the non-synchronous observer-based output feedback control of nonlinear systems by T-S fuzzy affine models. In order to estimate the unmeasurable system states and handle the non-synchronization issue of premise variables between the observer/controller and the original system, a new non-synchronous piecewise fuzzy affine observer is firstly synthesized to estimate the unmeasurable system state variables. Via utilizing some elegant matrix inequalities with convexification techniques, new observer-based piecewise output feedback controller design results are proposed within the framework of piecewise quadratic Lyapunov functions. Simulation studies are given to illustrate the validity of the developed approach.

PolyStokes: A Polynomial Model Reduction Method for Viscous Fluid Simulation

Standard liquid simulators apply operator splitting to independently solve for pressure and viscous stresses, a decoupling that induces incorrect free surface boundary conditions. Such methods are unable to simulate fluid phenomena reliant on the balance of pressure and viscous stresses, such as the liquid rope coil instability exhibited by honey. By contrast, unsteady Stokes solvers retain coupling between pressure and viscosity, thus resolving these phenomena, albeit using a much larger and thus more computationally expensive linear system compared to the decoupled approach. To accelerate solving the unsteady Stokes problem, we propose a reduced fluid model wherein interior regions are represented with incompressible polynomial vector fields. Sets of standard grid cells are consolidated into super-cells, each of which are modelled using a quadratic field of 26 degrees of freedom. We demonstrate that the reduced field must necessarily be at least quadratic, with the affine model being unable to correctly capture viscous forces. We reproduce the liquid rope coiling instability, as well as other simulated examples, to show that our reduced model is able to reproduce the same fluid phenomena at a smaller computational cost. Futhermore, we performed a crowdsourced user survey to verify that our method produces imperceptible differences compared to the full unsteady Stokes method.

Human-centered designed communication tools for obesity prevention in early life

ObjectiveHow we communicate about obesity is critical as treatment paradigms shift upstream. We previously identified parental perceptions, concerns, beliefs, and communication preferences about early life obesity risk. We engaged parents of children 0 to 24 months of age and pediatricians from Indianapolis, Indiana, USA in the co-design of messages and tools that can be used to facilitate parent/provider conversations about early life obesity prevention. MethodsFrom April to June 2021, we conducted a series of co-design workshops with parents of children ages 0 to 24 months and pediatricians to identify their preferences for communicating obesity prevention in the setting of a pediatric well visit. Human-centered design techniques, including affinity diagraming and model building, were used to inform key elements of a communication model and communication strategy messages. These elements were combined and refined to create prototype tools that were subsequently refined using stakeholder feedback. ResultsParent participants included 11 mothers and 2 fathers: 8 white, 4 black, and 1 Asian; median age 33 years with 38% reporting annual household incomes less than $50,000. Pediatricians included 7 female and 6 male providers; 69% white. Through an iterative process of co-design, we created an exam room poster that addresses common misconceptions about infant feeding, sleep and exercise, and a behavior change plan to foster parent/provider collaboration focused on achieving children’s healthy weight. ConclusionsOur hands-on, collaborative approach may ultimately improve uptake, acceptability and usability of early life obesity interventions by ensuring that parents remain at the center of prevention efforts.

Sampling‐based 3‐D line‐of‐sight PWA model predictive control for autonomous rendezvous and docking with a tumbling target

AbstractThis article introduces a line‐of‐sight (LOS)‐Euler rendezvous and docking (RVD) framework to dock with a tumbling target under several RVD constraints. By a double‐loop control scheme, the chaser's position is controlled to track the target's docking port which is coupled with its rotation. The chaser's attitude is driven to track the target's rotation, while satisfying the field of view constraintwhich is coupled with the LOS range. These complex couplings are linearly described in the proposed framework. To this end, the 6 DoF information interactions among the sensor measurements, states, and RVD constraints need no transformation and linearization. Consider the online piecewise affine (PWA) model predictive controller (MPC) may be unsolvable under complex constraints, which is caused by the accumulated prediction error, a sampling‐based method is proposed. The linear predictions are driven to the closer neighborhoods of the actual nonlinear states by constructing directional sampling intervals. Besides, a singularity free strategy is provided to realize continuous tracking with crossing the singularities of angle states. Detailed numerical simulations illustrate the validity of the proposed methods.

Development of in silico classification models for binding affinity to the glucocorticoid receptor

The endocrine disrupting properties of chemicals acting through the glucocorticoid receptor (GR) have attracted considerable interest. Since there are few data for most chemicals on their endocrine properties in silico approaches seem to be the most appropriate tool for screening and prioritizing chemicals for planning further experiments. In this work, we developed classification models for binding affinity to the glucocorticoid receptor using the counterpropagation artificial neural network method. We considered two series of 142 and 182 compounds and their binding affinity to the glucocorticoid receptor as agonists and antagonists, respectively. The compounds belong to different chemical classes. The compounds were represented by a set of descriptors calculated with the DRAGON program. The clustering structure of sets was studied with standard principal component method. A weak separation between binders and non-binders was found. Another classification model was developed using the counterpropagation artificial neural network method (CPANN). The final classification models developed were well balanced and showed a high level of accuracy, with 85.7% of GR agonist and 78.9% of GR antagonist correctly assigned in leave-one-out cross-validation.

CATs++: Boosting Cost Aggregation with Convolutions and Transformers.

Cost aggregation is a process in image matching tasks that aims to disambiguate the noisy matching scores. Existing methods generally tackle this by hand-crafted or CNN-based methods, which either lack robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields and inadaptability. In this paper, we introduce Cost Aggregation with Transformers (CATs) to tackle this by exploring global consensus among initial correlation map with the help of some architectural designs that allow us to benefit from global receptive fields of self-attention mechanism. To this end, we include appearance affinity modeling, which helps to disambiguate the noisy initial correlation maps. Furthermore, we introduce some techniques, including multi-level aggregation to exploit rich semantics prevalent at different feature levels and swapping self-attention to obtain reciprocal matching scores to act as a regularization. Although CATs can attain competitive performance, it may face some limitations, i.e., high computational costs, which may restrict its applicability only at limited resolution and hurt performance. To overcome this, we propose CATs++, an extension of CATs. Concretely, we introduce early convolutions prior to cost aggregation with a transformer to control the number of tokens and inject some convolutional inductive bias, then propose a novel transformer architecture for both efficient and effective cost aggregation, which results in apparent performance boost and cost reduction. With the reduced costs, we are able to compose our network with a hierarchical structure to process higher-resolution inputs. We show that the proposed method with these integrated outperforms the previous state-of-the-art methods by large margins. Codes and pretrained weights are available at: https://ku-cvlab.github.io/CATs-PlusPlus-Project-Page/.

Fragility-Free Prescribed Performance Control Without Approximation Applied to Waverider Aerocraft

In this article, a fragility-free prescribed performance control (PPC) approach is proposed for unknown disturbed non-affine systems with application to flight control of waverider aerocraft (WA). The main improvement is to develop a prescribed funnel containing additional re-adjusting terms, which is able to autonomously re-adjust its shape, such that the tracking error, whose value may increase due to parametric perturbations and external disturbances, is always constrained within the prescribed funnel, capable of guaranteeing, for any initial system condition, (1) avoidance of security fragility problem associated with the existing PPC, (2) finite-time prescribed performance concerning tracking errors, and (3) independent of affine model formulation and function approximation. Finally, the addressed design is applied to WA, and compared simulations with practical examples are presented to show the superiority.

CO2 Hydrate Formation Kinetics in the Presence of a Layered Double Hydroxide Nanofluid

AbstractThe effects of a hybrid nanofluid (Cu‐Al layered double hydroxide (LDH)) on the CO2 hydrate formation kinetics was investigated at three different nanofluid concentrations (0.25–1.0 wt %). The Cu‐Al LDH nanofluid was prepared using a one‐step co‐precipitation technique. The addition of the LDH nanofluid reduces the induction time and enhances the hydrate formation kinetics. The maximum reduction in the induction time (by 91.08 %) was observed at the optimal nanofluid concentration of 0.5 wt %, compared to water. A chemical affinity model was implemented to predict the experimental data for CO2 hydrate formation in the presence of the different LDH nanofluid concentrations. An artificial neural network‐based Levenberg‐Marquardt model predicts the experimental data with higher accuracy than the scaled conjugate gradient model. The results indicate a strong dependency of the hydrate formation kinetics on the LDH nanofluid concentrations.

Can one hear the shape of a target zone?

We develop an exchange rate target zone model with finite exit time and non-Gaussian tails. We show how the tails are a consequence of time-varying investor risk aversion, which generates mean-preserving spreads in the fundamental distribution. We solve explicitly for stationary and non-stationary exchange rate paths, and show how both depend continuously on the distance to the exit time and the target zone bands. This enables us to show how central bank intervention is endogenous to both the distance of the fundamental to the band and the underlying risk. We discuss how the feasibility of the target zone is shaped by the set horizon and the degree of underlying risk, and we determine a minimum time at which the required parity can be reached. We prove that increases in risk beyond a certain threshold can yield endogenous regime shifts where the “honeymoon effects” vanish and the target zone cannot be feasibly maintained. None of these results can be obtained by means of the standard Gaussian or affine models. Numerical simulations allow us to recover all the exchange rate densities established in the target zone literature. The generality of our framework has important policy implications for modern target zone arrangements.

Analogue of Ramanujan’s function k(τ) for the cubic continued fraction

We study the modularity of the function [Formula: see text], where [Formula: see text] is Ramanujan’s cubic continued fraction. It is an analogue of Ramanujan’s function [Formula: see text], where [Formula: see text] is the Rogers–Ramanujan continued fraction. We first prove the modularity of [Formula: see text] and express [Formula: see text] and [Formula: see text] in terms of [Formula: see text]. Subsequently, we find modular equations of [Formula: see text] of level [Formula: see text] for every positive integer [Formula: see text] by using affine models of modular curves. Finally, we demonstrate that the value of [Formula: see text] generates the ray class field over an imaginary quadratic field modulo 2 for some [Formula: see text] in an imaginary quadratic field.

Swelling Effect on the Yielding, Elasticity, and Fracture of Double-Network Hydrogels with an Inhomogeneous First Network

Typical double-network (DN) gels are formed by free radical polymerization, and the constituent network strands have a wide molecular weight distribution. It is important to clarify what determines the specific mechanical behaviors of DN gels with a strongly inhomogeneous first network, including the criteria for the occurrence of yielding and strain hardening during tensile deformation, as well as their impact on the fracture toughness. In this work, we first studied the influence of swelling ratio λs of the first network synthesized from radical polymerization on the yielding and strain hardening of DN and triple-network (TN) gels by tensile tests. We observed scaling relations for the yielding stretch ratio λy ∝ λs–1 and engineering yielding stress σy ∝ λs–2, like those found for DN hydrogels with a homogeneous first-network structure. Furthermore, we found a universal relationship λyσy ∝ λs–3 that is independent of the cross-linking structure of the first network. We found that these scaling relations agree well with the theoretical predictions derived from a simple one-dimensional affine model for the fracture of a homogeneous network, suggesting the affine swelling effect on the fracture of network strands. Next, we showed that swelling induces more tension in the inhomogeneous network than in the homogeneous network, suggesting non-affine swelling behavior at the network strand scale for the inhomogeneous network. We discussed the molecular origin of the discrepancy between the affine swelling effect on the fracture of network strands and the non-affine swelling behavior for strand tension. Finally, we investigated the influence of swelling ratio λs on the local yielding behavior ahead of the crack tip and the fracture toughness of the DN and TN hydrogels using pure shear tests. We found that crack-tip yielding zone size hy ∝ λs1 and fracture energy Γ ∝ λs–2. Furthermore, we found that the relationship Γhy∝λs−3 is also independent of cross-linking structure and obeys a universal relationship with the true yielding stress λyσy ∝ λs–3 determined from the tensile tests, which is in excellent agreement with nonlinear elastic fracture mechanics theory. This work provides important insight into the specific mechanical behaviors of the DN gels with an inhomogeneous first network.

Towards optimization of 5G NR transport over fiber links performance in 5G Multi-band Networks: An OMSA model approach

The application of analog radio over fiber (A-RoF) systems for 5G new radio (NR) multiband waveforms presents challenges, including nonlinearity introduced by the Mach Zehnder Modulator and the dispersion and attenuation of the optical signal over long fiber lengths. To address these challenges, this paper proposes a new version of the magnitude-selective affine (MSA) model, named the optimized magnitude-selective affine (OMSA) model, which incorporates a power-reliant weighting function to improve performance while reducing complexity via Digital Predistortion (DPD). The OMSA model is tested using 5G NR signals at 10 GHz with 50 MHz and flexible-waveform signals at 2.14 GHz with a 20 MHz bandwidth, transmitted over a 10 km fiber length using a Mach Zehnder Modulator and a 1550 nm optical carrier. The performance of the OMSA model is compared to the MSA and generalized memory polynomial (GMP) models in terms of adjacent channel power ratio, error vector magnitude, and complexity. The results show that the OMSA model outperforms the MSA and GMP models in terms of performance reducing the error vector magnitude to 1.9% as compared to 4% and 3% in case of GMP and MSA respectively. Additionally, in terms of complexity measured via coefficients, OMSA is 168 times more efficient as compared to GMP and offers 1.93 times higher efficiency as compared to MSA while maintaining lower complexity. The experimental demonstration of 5G NR multiband waveforms over A-RoF links using the OMSA model represents a significant step towards the practical implementation of high-performance, low-complexity 5G NR systems over fiber-optic links and provides a promising solution to the challenges associated with DPD linearization for A-RoF systems.

Selectivity and affinity of heavy metals and radiometals for organic biomass: Implications for water remediation

ABSTRACT Due to both natural and anthropogenic causes, waterways have a history of contamination with different heavy metals and radiometals. Among these, thorium (Th,) uranium (U,) arsenic (As,) and strontium (Sr) are noteworthy threats to humans due to chemical and radiological toxicity. Previous research has focused on inorganic materials to remove these metals. However, the use of recyclable and biodegradable waste materials to remove toxic metals has risen. This study seeks to use pistachio shells as a model for heavy metal selectivity and affinity for organic biomasses. The influence of kinetics and pH on selectivity/affinity of the shell for the metals was investigated. The individual metal affinity seen was Th > U > As > Sr. Selectivity for Th and U over As and Sr was seen at pH < 6, beginning to equalize at pH > 6. The maximum uptake for all metals tested occurs at pH 5 and t ≥2 h. Uptake of these metals follows pseudo-second order, intraparticle kinetics, and Freundlich isotherm. Finally, the selectivity of the shells for heavy metals was investigated using drinking water and seawater samples, with concurrent pronounced uptake of actinides and Sr being observed.