Affine Model Research Articles

Optimal design of a driver assistance controller based on surrounding vehicle’s social behavior game model

Driver assistance control in the cut-in scenarios is challenging, since the controller needs to ensure driving safety and avoid unnecessary intervention, while considering the interaction with surrounding vehicles. This paper proposes an optimal driver assistance controller considering the social behaviors of the surrounding vehicles to assist the drivers in the cut-in scenarios. To model the social behavior of the surrounding vehicle, we first formulate the interaction between the semi-autonomous vehicle and the surrounding vehicle as a misinformation game, which is achieved by assuming the surrounding vehicle is interacting with a hypothetical vehicle under the framework of non-cooperative game. Then, adaptive dynamic programming theory is utilized to find the Nash equilibrium, which is represented by deep neural networks and solved iteratively. Based on the established social behavior model and the nonlinear driver-vehicle dynamic model, an affine input nonlinear system model is obtained for the design of driver assistance controller, and the optimal assistance control strategy is also derived under the structure of adaptive dynamic programming. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can assist the driver in the cut-in scenario while addressing different social interactions with the surrounding vehicles. Importantly, by taking into account the surrounding vehicle’s social behavior through our established social behavior model, our proposed strategy significantly outperforms the no-interaction strategy both in terms of driving safety and intervention degree, validating its effectiveness and superiority.

Hybrid Model Linearization Predictive Control for DC Bus Stabilization in Spacecraft Combined Power System

In spacecraft, facing different loading conditions of subsystems, especially of permanent magnet synchronous motor (PMSM) system, the bus voltage has large fluctuation, which will seriously affect the power quality of the spacecraft combined power system. Therefore, it is necessary to introduce a bidirectional DC–DC converter to suppress the fluctuation of the bus voltage. Aiming at the need of controlling the bidirectional DC–DC converter, a hybrid model linearization predictive control (HMLPC) algorithm is proposed. According to the strong nonlinear hybrid characteristics of the bidirectional DC–DC converter, an optimal linear approximation model at the sampling point is established using the Jacobian matrix, based on which a piecewise affine (PWA) model is established for Buck and Boost mode, and then, a hybrid linearization predictive model is constructed. Ultimately, by introducing a two-period PWA and multistep prediction strategy, proposing a prediction error, and solving the constrained finite time optimization control (CFTOC) problem online, an optimal control law is derived. In addition, a stability analysis is provided for the proposed control algorithm. In this article, a simulative validation and a comparative analysis are presented; moreover, a combined power test platform is built to validate the proposed control algorithm. Both simulation and experiment results show that the HMLPC algorithm can effectively suppress the fluctuation of bus voltage and enhance the anti-interference ability of the system.

Novel activity of human BChE: Lipid hydrolysis

Acetylcholinesterase and butyrylcholinesterase (BChE) typically hydrolyze the neurotransmitter acetylcholine. The multifunctional enzyme BChE is associated with lipid metabolism through an undefined mechanism. Based on lipid-related studies and by comparing the structural similarities between lipases and BChE we postulated that the association of BChE with lipid metabolism could occur through hydrolytic activity. Utilizing purified BChE enzymes from different sources and several lipases as controls, the ability of BChE to hydrolyze 4-methylumbelliferyl (4-mu) palmitate is investigated. Using lectin affinity, inhibition kinetics, and molecular modeling, we demonstrated that purified BChE hydrolyzed 4-mu palmitate at pH 8 as effectively as wheat germ lipase. The affinity Km value of the enzymes for 4-mu palmitate as substrate is found as 10.4 μM, 34.2 μM, 129.8 μM, and 186 μM for wheat germ lipase, purified BChE, pancreatic lipase, and commercial BChE, respectively. Analysis of the inhibitory effect of 4-mu palmitate on BChE using butyrylthiocholine as substrate revealed competitive inhibition with Ki and IC50 values of 448 μM and 987.2 μM, respectively. The binding affinity and interactions of 4-mu palmitate with BChE and pancreatic lipase were predicted by molecular docking. These results suggest that BChE possesses lipolytic activity. The possibility that BChE hydrolyzes not only 4-mu palmitate but also other types of lipids will lead to a new approach to those disease states associated with increased BChE activity/expression.

Modelling USA Age-Cohort Mortality: A Comparison of Multi-Factor Affine Mortality Models

Affine mortality models are well suited for theoretical and practical application in pricing and risk management of mortality risk. They produce consistent, closed-form stochastic survival curves allowing for the efficient valuation of mortality-linked claims. We model USA age-cohort mortality data using five multi-factor affine mortality models. We focus on three-factor models and compare four Gaussian models along with a model based on the Cox–Ingersoll–Ross (CIR) process, allowing for Gamma-distributed mortality rates. We compare and assess the Gaussian Arbitrage-Free Nelson–Siegel (AFNS) mortality model, which incorporates level, slope and curvature factors, and the canonical Gaussian factor model, both with and without correlations in the factor dynamics. We show that for USA mortality data, the probability of negative mortality rates in the Gaussian models is small. Models are estimated using discrete time versions of the models with age-cohort data capturing variability in cohort mortality curves. Poisson variation in mortality data is included in the model estimation using the Kalman filter through the measurement equation. We consider models incorporating factor dependence to capture the effects of age-dependence in the mortality curves. The analysis demonstrates that the Gaussian independent-factor AFNS model performs well compared to the other affine models in explaining and forecasting USA age-cohort mortality data.

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.

Multiassay Profiling of a Focused Small Molecule Library Reveals Predictive Bidirectional Modulation of the lncRNA MALAT1 Triplex Stability In Vitro.

The rapidly accelerating characterization of RNA tertiary structures has revealed their pervasiveness and active roles in human diseases. Small molecule-mediated modulation of RNA tertiary structures constitutes an attractive avenue for the development of tools for therapeutically targeting and/or uncovering the pathways associated with these RNA motifs. This potential has been highlighted by targeting of the triple helix present at the 3'-end of the noncoding RNA MALAT1, a transcript implicated in several human diseases. This triplex has been reported to decrease the susceptibility of the transcript to degradation and promote its cellular accumulation. While small molecules have been shown to bind to and impact the stability of the MALAT1 triple helix, the small molecule properties that lead to these structural modulations are not well understood. We designed a library utilizing the diminazene scaffold, which is underexplored but precedented for nucleic acid binding, to target the MALAT1 triple helix. We employed multiple assays to holistically assess what parameters, if any, could predict the small molecule affinity and effect on triplex stability. We designed and/or optimized competition, calorimetry, and thermal shift assays as well as an enzymatic degradation assay, the latter of which led to the discovery of bidirectional modulators of triple helix stability within the scaffold-centric library. Determination of quantitative structure-activity relationships afforded predictive models for both affinity- and stability-based assays. This work establishes a suite of powerful orthogonal biophysical tools for the evaluation of small molecule:RNA triplex interactions that generate predictive models and will allow small molecule interrogation of the growing body of disease-associated RNA triple helices.

Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential

ABSTRACT Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model’s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r 2 = 0.9468, and RMSE = 1.26 kJ/mol.

MLD–MPC for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit Using Subspace Identification

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.

Multiplexed MPC attitude control of a moving mass satellite using dual-rate piecewise affine model

This paper proposes a Multiplexed MPC algorithm for the dual-rate piecewise affine (PWA) model to control the attitude of a low-Earth-orbit (LEO) moving mass satellite. Although still facing an inherent inertial disturbance problem, the moving mass technology is a promising way to counter the strong aerodynamic torques in LEO. To make the satellite attitude fully actuated, a magnetorquer is used to cooperate with the moving mass system, resulting in a two-input control system. Through a strategy that updates inputs asynchronously, the proposed algorithm effectively balances the inertial disturbances caused by mass motions, the system's dynamic performance, and the controller's computational complexity. Error attitude kinematic and dynamic equations are derived, to convert the tracking problem into an equivalent regulation problem. The PWA approximation is used to transform the nonlinear attitude control equations described by error modified Rodrigues parameter to a linear state-space model for MPC design. Then, a new form of Multiplexed MPC, namely dual-rate PWA-MMPC, is developed for this dual-rate PWA model, which updates two control inputs at different rates. Despite only finding sub-optimal solutions to the original problem by stability analysis, this algorithm outperforms other control strategies through its ability to coordinate multiple inputs, deal with constraints, and reduce computational complexity. Hardware-in-the-loop simulations are conducted to demonstrate the effectiveness of the proposed methodology for the attitude control.

Parameter Estimation for SPDEs Driven by Cylindrical Stable Processes

We consider infinite dimensional extension of affine models with heavy tails in finance. We study several estimators of the drift parameter in the stochastic partial differential equation driven by cylindrical stable processes. We consider several sampling schemes. We also consider random sampling scheme, e.g, when the solution process is observed at the arrival times of a Poisson process. We obtain the consistency and the asymptotic normality of the estimators.

Do AutoML-Based QSAR Models Fulfill OECD Principles for Regulatory Assessment? A 5-HT1A Receptor Case.

The drug discovery and development process requires a lot of time, financial, and workforce resources. Any reduction in these burdens might benefit all stakeholders in the healthcare domain, including patients, government, and companies. One of the critical stages in drug discovery is a selection of molecular structures with a strong affinity to a particular molecular target. The possible solution is the development of predictive models and their application in the screening process, but due to the complexity of the problem, simple and statistical models might not be sufficient for practical application. The manuscript presents the best-in-class predictive model for the serotonin 1A receptor affinity and its validation according to the Organization for Economic Co-operation and Development guidelines for regulatory purposes. The model was developed based on a database with close to 9500 molecules by using an automatic machine learning tool (AutoML). The model selection was conducted based on the Akaike information criterion value and 10-fold cross-validation routine, and later good predictive ability was confirmed with an additional external validation dataset with over 700 molecules. Moreover, the multi-start technique was applied to test if an automatic model development procedure results in reliable results.

Sound source localization using multiple ad hoc distributed microphone arrays.

Sound source localization is crucial for communication and sound scene analysis. This study uses direction-of-arrival estimates of multiple ad hoc distributed microphone arrays to localize sound sources in a room. An affine mapping between the independent array estimates and the source coordinates is derived from a set of calibration points. Experiments show that the affine model is sufficient to locate a source and can be calibrated to physical dimensions. A projection of the local array estimates increases localization accuracy, particularly further away from the calibrated region. Localization tests in three dimensions compare the affine approach to a nonlinear neural network.

Piecewise Affine Modeling and Robust Model Predictive Control of a Distillation Column Considering Multiple Prediction Trajectories

Piecewise Affine Modeling and Robust Model Predictive Control of a Distillation Column Considering Multiple Prediction Trajectories

DSAGLSTM-DTA: Prediction of Drug-Target Affinity using Dual Self-Attention and LSTM

The research on affinity between drugs and targets (DTA) aims to effectively narrow the target search space for drug repurposing. Therefore, reasonable prediction of drug and target affinities can minimize the waste of resources such as human and material resources. In this work, a novel graph-based model called DSAGLSTM-DTA was proposed for DTA prediction. The proposed model is unlike previous graph-based drug-target affinity model, which incorporated self-attention mechanisms in the feature extraction process of drug molecular graphs to fully extract its effective feature representations. The features of each atom in the 2D molecular graph were weighted based on attention score before being aggregated as molecule representation and two distinct pooling architectures, namely centralized and distributed architectures were implemented and compared on benchmark datasets. In addition, in the course of processing protein sequences, inspired by the approach of protein feature extraction in GDGRU-DTA, we continue to interpret protein sequences as time series and extract their features using Bidirectional Long Short-Term Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly, DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to obtain comprehensive representations of proteins, in which the final hidden states for element in the batch were weighted with the each unit output of LSTM, and the results were represented as the final feature of proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block for final prediction. The proposed model was evaluated on different regression datasets and binary classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.

Learning to Model Pixel-Embedded Affinity for Homogeneous Instance Segmentation

Homogeneous instance segmentation aims to identify each instance in an image where all interested instances belong to the same category, such as plant leaves and microscopic cells. Recently, proposal-free methods, which straightforwardly generate instance-aware information to group pixels into different instances, have received increasing attention due to their efficient pipeline. However, they often fail to distinguish adjacent instances due to similar appearances, dense distribution and ambiguous boundaries of instances in homogeneous images. In this paper, we propose a pixel-embedded affinity modeling method for homogeneous instance segmentation, which is able to preserve the semantic information of instances and improve the distinguishability of adjacent instances. Instead of predicting affinity directly, we propose a self-correlation module to explicitly model the pairwise relationships between pixels, by estimating the similarity between embeddings generated from the input image through CNNs. Based on the self-correlation module, we further design a cross-correlation module to maintain the semantic consistency between instances. Specifically, we map the transformed input images with different views and appearances into the same embedding space, and then mutually estimate the pairwise relationships of embeddings generated from the original input and its transformed variants. In addition, to integrate the global instance information, we introduce an embedding pyramid module to model affinity on different scales. Extensive experiments demonstrate the versatile and superior performance of our method on three representative datasets. Code and models are available at https://github.com/weih527/Pixel-Embedded-Affinity.

A stable block adjustment method without ground control points using bound constrained optimization

ABSTRACT Block adjustment is a key technology for achieving large-scale accurate mapping of space via remote sensing. When block adjustment without ground control points (GCPs) is conducted, the adjustment model has large condition number of design matrix, and an ill-conditioned normal equation, which eventually leads to abnormal convergence of direct least squares solution. In this study, we investigated block adjustment without GCPs based on Rational Function Model (RFM), and applied image space affine model of RFM as the systematic error compensation model of single scene imagery. The value range of each parameter of the affine model was determined by calculating the inverse projection errors of tie points and the adjustment model parameters were solved by the optimization method with attached inequalities constraint. Finally, the abnormal convergence problem was solved by reducing the degree of freedom of convergence. Comparative tests of block adjustment without GCPs using various methods were conducted using multiple regional imagery from Ziyuan-3 (ZY-3), Tianhui-1 (TH-1), Luojia-1 (LJ-1) and Gaofen-3 (GF-3). The results show that the proposed method can achieve rapid and correct convergence, and has strong practicability.

Jump dynamics, spillover effect and option valuation

In this paper, we propose an affine discrete-time model that incorporates the jump process and spillover effect for valuing the 50 ETF options in China. Based on the proposed model, a closed-form solution is also derived for the new dynamics of underlying asset, which facilitates option pricing. The empirical results show that the proposed model offers greater economic benefit with reduced pricing errors than the traditional benchmark models, including the popular HNGARCH model of Heston and Nandi (2000), GARV model of Christoffersen et al. (2014), and BPJVM model of Christoffersen et al. (2015). Our finding is important for financial risk management and investment in Chinese derivatives market.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

Calcium Ion Binding to the Mutants of Calmodulin: A Structure-Based Computational Predictive Model of Binding Affinity Using a Charge Scaling Approach in Molecular Dynamics Simulation.

The binding of calcium ions (Ca2+) to the calcium-binding proteins (CBPs) controls a plethora of regulatory processes. Among the roles played by CBPs in several diseases, the onset and progress of some cardiovascular diseases are caused by mutations in calmodulin (CaM), an important member of CBPs. Rationalization and prediction of the binding affinity of Ca2+ ions to the CaM can play important roles in understanding the origin of cardiovascular diseases. However, there is no robust structure-based computational method for predicting the binding affinity of Ca2+ ions to the different forms of CBPs in general and CaM in particular. In the current work, we have devised a fast yet accurate computational technique to accurately calculate the binding affinity of Ca2+ to the different forms of CaM. This method combines the well-known molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method and a charge scaling approach developed by previous authors that takes care of the polarization of CaM and Ca2+ ions. Our detailed analysis of the different components of binding free energy shows that subtle changes in electrostatics and van der Waals contribute to the difference in the binding affinity of mutants from that of the wild type (WT), and the charge scaling approach is superior in calculating these subtle changes in electrostatics as compared to the nonpolarizable force field used in this work. A statistically significant regression model made from our binding free energy calculations gives a correlation coefficient close to 0.8 to the experimental results. This structure-based predictive model can open up a new strategy to understand and predict the binding of Ca2+ to the mutants of CBPs, in general.

Luojia-1 Nightlight Image Registration Based on Sparse Lights

When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to the lack of structural information. In order to address this issue, this study devises an easy-to-implement engineering solution that allows for the registration of sparse light areas with high efficiency while guaranteeing accuracy in non-sparse light areas. The proposed method first extracts the sparsely distributed light point positions through use of roundness detection and the centroid method. Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. Through experimentation it was found that, compared with traditional registration methods, the proposed method is more reliable and has a wider distribution in sparse light areas. Finally, through the registration test of 275 scenes of nightlight images of China from Luojia-1, the coverage ratio of the tie points was increased from 59.3% from the traditional method to 95.3% in this study and the accuracy of the block adjustment was 0.63 pixels, which verifies the effectiveness of the method. The proposed method provides a basis for the registration, block adjustment, and mosaicking of nightlight images.