Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform

In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker product without system noise. Secondly, the augmented system errors are illustratively charactered by Gaussian white noise. Thirdly, at the expanded space a creative high-order Kalman filter is delicately designed, which consists of high-order Taylor expansion, introducing magical intermediate variables, representing linear systems converted from strongly nonlinear systems, designing Kalman filter, etc. The performance of the proposed filter will be much better than one of EKF, because it uses more information than EKF. Finally, its promise is verified through commonly used digital simulation examples.

Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

Neighborhood Cohesion and the Mental Health of Multimorbid Older Adults: CLSA Path Analysis Through Loneliness

More older adults with multimorbidity are aging in place than ever before. Their mental health may be affected by housing and neighborhood factors. In this paper, we use structural equation modelling (SEM) to examine how the physical environment influences life satisfaction and depressive symptoms in two separate models. We included social environment (i.e., social support, social participation, walking) and loneliness as intermediate variables. Data were drawn from baseline and the first follow-up (after 3-4 years) of the Canadian Longitudinal Study on Aging (CLSA). Participants were N=14,301 adults aged □65 with □2 chronic illnesses. Good model fit were found after controlling for age, sex, education and baseline values (TFI=1.00; CFI=1.00; RMSEA<0.001; SRMR<0.001). The total effects of housing quality (Btotal=0.08,-0.07) and neighborhood cohesion (Btotal=0.03,-0.06) were weak but statistically significant in the expected direction. Together, the intermediate variables explained 21-31% of the total effects of housing quality and 67-100% of the total effects of neighborhood cohesion. Loneliness explains 27-29% of the total effects of physical environment on mental health, whereas walking explained a mere 0.4-0.9% of their total effects. Walking did not mediate between housing quality and mental health outcomes. Overall, the results support our path analysis framework: physical environment -> social environment -> loneliness -> mental health. Our model provided excellent explanations of the effects of neighborhood cohesion, especially on life satisfaction. If these associations reflect causal effects, community-based age-friendly interventions should focus on neighborhood cohesion and loneliness to promote the well-being of older adults who are aging in place with multimorbidity.

Bayesian Uncertainty Identification of Model Parameters for the Jointed Structures with Nonlinearity

Jointed structures in engineering naturally perform with some of nonlinearity and uncertainty, which significantly affect the dynamic characteristics of the structural system. In this paper, the method of Bayesian uncertainty identification of model parameters for the jointed structures with local nonlinearity is proposed. Firstly, the nonlinear stiffness and damping of the joints under the random excitation are represented with functions of excitation magnitude in terms of the equivalent linearization. The process of uncertainty identification is separated from the representation of local nonlinearity. In this way, the dynamic behavior of the joints is penetratingly characterized instead of ascribing the nonlinearity to uncertainty. Secondly, a variable-expanded Bayesian (VEB) method is originally proposed to identify the mixed of aleatory and epistemic uncertainties of model parameters. Different from traditional Bayesian identification, the aleatory uncertainties of model parameters are identified as one of the most important parts rather than only measurement noise of output. Notablely, a series of intermediate variables are introduced to expand the parameter with aleatory uncertainty in order to overcome the difficulty of establishing the likelihood function. Moreover, a 3-DOF numerical example is illustrated with case studies to verify the proposed method. The influence of observed sample size and prior distribution selection on the identification results is tested. Furthermore, an engineering example of the jointed structure with rubber isolators is performed to show the practicability of the proposed method. It is indicated that the computational model updated with the accurately identified parameters with both nonlinearity and uncertainty has shown the excellent predictive capability.

Torque ripple optimization for synchronous reluctance motors based on a virtual permanent magnet harmonic machine model

The torque ripple is affected by both the stator and the rotor magnetic field harmonics. In synchronous reluctance motors (SynRM), there are only rotor permeance harmonics existing on the rotor side for the absence of the rotor windings. Since the asymmetric rotor flux barriers are widely applied in the SynRM rotor, it is difficult to calculate the rotor permeance accurately by the analytical method. In this article, the effects of the rotor permeance harmonics on the air-gap magnetic field are studied by a virtual permanent magnet harmonic machine (VPMHM), which is a finite-element (FE) based magnetostatic analysis model. The air-gap flux density harmonics produced by the SynRM rotor are extracted from the VPMHM model and used as the intermediate variables for the torque ripple optimization. The proposed method does not need to solve the transient process of motor motion. Hence, the time of the optimization process can be significantly shortened. Finally, a full electric cycle is simulated by dynamic FE simulation, and the torque ripple is proved to be effectively reduced.

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

Effect of digital banking-related customer experience on banks’ financial performance during Covid-19: a perspective from Vietnam

Purpose This study aims to investigate customer experience (CE) and its relationship with intermediate variables to analyze the impact of digital banking (DB) on banks’ financial performance (FP) before Covid-19 and during the lockdown in Vietnam. Design/methodology/approach These research data are from a survey of Vietnamese customers. The survey was deployed to a sample of 238 and 218 customers of 20 Vietnamese commercial banks via email in 2018Q4 and 2020Q2, respectively. FP is measured using banks’ quarterly financial statements before Covid-19 and during the lockdown. Findings CE with DB had a significant and positive impact on FP via customer satisfaction before Covid-19, while the other two intermediate variables (word-of-mouth [WoM] and trust) had no considerable impact. During the lockdown, only WoM had a positive impact on FP. These findings indicate that before Covid-19, when customers could easily interact with their bank through many touchpoints, customer satisfaction with DB services created higher FP for the bank. However, during the lockdown, DB became the customer’s main touchpoint and WoM mediated the CE–FP relationship. Originality/value During the national lockdown from the beginning of the Covid-19 pandemic in January 2020, customers in Vietnam may have had different experiences with DB when no alternate modes of payment were available. The study uses Covid-19 as a moderator variable to offer different viewpoints and findings related to CE with DB and its impact on FP.

Restructured single parabolic band model for quick analysis in thermoelectricity

The single parabolic band (SPB) model has been widely used to preliminarily elucidate inherent transport behaviors of thermoelectric (TE) materials, such as their band structure and electronic thermal conductivity, etc. However, in the SPB calculation, it is necessary to determine some intermediate variables, such as Fermi level or the complex Fermi-Dirac integrals. In this work, we establish a direct carrier-concentration-dependent restructured SPB model, which eliminates Fermi-Dirac integrals and Fermi level calculation and emerges stronger visibility and usability in experiments. We have verified the reliability of such restructured model with 490 groups of experimental data from state-of-the-art TE materials and the relative error is less than 2%. Moreover, carrier effective mass, intrinsic carrier mobility and optimal carrier concentration of these materials are systematically investigated. We believe that our work can provide more convenience and accuracy for thermoelectric data analysis as well as instructive understanding on future optimization design.

Disparity Analysis for Single Variable Interventions

A recent literature in sociology and epidemiology introduces the `targeted disparity': the extent to which socio-demographic inequalities would persist under an intervention to equalize some intervening variable on the path from the demographic marker to an outcome. In this paper, I propose a unified framework with which to understand targeted interventions. This framework helps researchers map a graphical claim about an intervention in terms of blocking causal and non-causal pathways from a demographic marker to an outcome onto mathematical claims which involve defining an intervention in terms of a probability function. Further, I propose a new type of targeted disparity which intervenes on one of two intermediate variables, and generalize this to describe an intervention that intervenes with respect to an arbitrary `intervention set'. Each of the interventions I describe can be understood as mapping onto a particular set of policy interventions, but can additionally be shown to decompose the total disparity between socio-demographic groups into cumulative pathways through a mediator. Finally, I propose a series of parametric and non–parametric estimation strategies that can be combined with machine-learning methods to estimate the disparities of interest.

A Causal Web between Chronotype and Metabolic Health Traits

Observational and experimental evidence has linked chronotype to both psychological and cardiometabolic traits. Recent Mendelian randomization (MR) studies have investigated direct links between chronotype and several of these traits, often in isolation of outside potential mediating or moderating traits. We mined the EpiGraphDB MR database for calculated chronotype–trait associations (p-value < 5 × 10−8). We then re-analyzed those relevant to metabolic or mental health and investigated for statistical evidence of horizontal pleiotropy. Analyses passing multiple testing correction were then investigated for confounders, colliders, intermediates, and reverse intermediates using the EpiGraphDB database, creating multiple chronotype–trait interactions among each of the the traits studied. We revealed 10 significant chronotype–exposure associations (false discovery rate < 0.05) exposed to 111 potential previously known confounders, 52 intermediates, 18 reverse intermediates, and 31 colliders. Chronotype–lipid causal associations collided with treatment and diabetes effects; chronotype–bipolar associations were mediated by breast cancer; and chronotype–alcohol intake associations were impacted by confounders and intermediate variables including known zeitgebers and molecular traits. We have reported the influence of chronotype on several cardiometabolic and behavioural traits, and identified potential confounding variables not reported on in studies while discovering new associations to drugs and disease.