Bias Term Research Articles

A new LSTNet-based temperature prediction model for permanent magnet

Permanent magnet synchronous motors (PMSMs) can effectively protect against demagnetization by accurate permanent magnet (PM) temperature prediction; nevertheless, due to the nonlinear properties and intricate internal structure of PMSMs, accurate PM temperature prediction methods still encounter difficulties. This paper proposes a new PM temperature prediction model (LSTNet-Improved) based on long- and short-term time series network (LSTNet) to increase the prediction accuracy of PM temperature. By adding a multi-scale convolutional (MSC) layer to the convolutional neural network (CNN) layer of LSTNet, the short-term detail-dependent information between PMSM variables can be obtained by the model, and adds a CNN skip (CNN-skip) layer in place of the gate recurrent unite skip (GRU-skip) layer to find the long- and short-term local repetitive patterns between the variables, which yields additional feature information. Furthermore, a nonlinear bias term (MNB) is added to the original multi-head attention layer in order to solve the complex nonlinear relationship between multivariate time series. These layers are applied after the CNN-skip layer and gated recurrent unite (GRU) layers, respectively. This allows the model to become more robust and automatically learn to the complex nonlinear relationship between the sequences, focus on the important feature information, and lessen the impact of redundant information on the PMs’ temperature prediction task. The experimental results show that the goodness of fit (R2), root mean square error (RMSE) and mean absolute error (MAE) of the proposed model are 99.93%, 14.55% and 9.45%, respectively, and compared with LSTNet, the R2 is improved by 0.09%, the RMSE and MAE are reduced by 7.03% and 6.51%. These results confirm that the model can correctly predict the PM temperature, efficiently extract both long- and short-term patterns among the variables of the PMSM, and effectively focus on the key feature information in the intricate nonlinear series.

Fresnel incoherent compressive holography toward 3D videography via dual-channel simultaneous phase-shifting interferometry.

Fresnel incoherent correlation holography (FINCH) enables high-resolution 3D imaging of objects from several 2D holograms under incoherent light and has many attractive applications in motionless 3D fluorescence imaging. However, FINCH has difficulty implementing 3D imaging of dynamic scenes since multiple phase-shifting holograms need to be recorded for removing the bias term and twin image in the reconstructed scene, which requires the object to remain static during this progress. Here, we propose a dual-channel Fresnel noncoherent compressive holography method. First, a pair of holograms with π phase shifts obtained in a single shot are used for removing the bias term noise. Then, a physic-driven compressive sensing (CS) algorithm is used to achieve twin-image-free reconstruction. In addition, we analyze the reconstruction effect and suitability of the CS algorithm and two-step phase-shift filtering algorithm for objects with different complexities. The experimental results show that the proposed method can record hologram videos of 3D dynamic objects and scenes without sacrificing the imaging field of view or resolution. Moreover, the system refocuses images at arbitrary depth positions via computation, hence providing a new method for fast high-throughput incoherent 3D imaging.

A bond swap algorithm for simulating dynamically crosslinked polymers

Materials incorporating covalent adaptive networks (CAN), e.g., vitrimers, have received significant scientific attention due to their distinctive attributes of self-healing and stimuli-responsive properties. Different from direct crosslinked systems, bivalent and multivalent systems require a bond swap algorithm that respects detailed balance, considering the multiple equilibria in the system. Here, we propose a simple and robust algorithm to handle bond swap in multivalent and multi-species CAN systems. By including a bias term in the acceptance of Monte Carlo moves, we eliminate the imbalance from the bond swap site selection and multivalency effects, ensuring the detailed balance for all species in the system.

Single‐shot phase‐shifting on Michelson interferometry for incoherent digital holography

AbstractA single‐shot phase‐shifting method in incoherent digital holography (IDH) based on Michelson interferometry is proposed herein. The proposed method uses polarization‐modulating optical elements and a polarization sensor in the Michelson interferometer optics to induce a geometric phase shift. It acquires four holograms with different geometric phase retardations in a single exposure to eliminate the bias and conjugate noise using a phase‐shifting technique. The proposed optical system enables real‐time bias and conjugate term removal from a recorded hologram without requiring sequential recording for phase shifting. Furthermore, because it is based on Michelson interferometry, it can be implemented using simple reflective optics without expensive or difficult‐to‐produce optical elements for single‐shot phase shifting. The principles of self‐interference and geometric phase shifting in the proposed optical structure were analyzed mathematically. A real‐time IDH‐recording experimental setup was constructed to verify the proposed method. The experimental results of single‐shot phase‐shifting IDH and hologram movie recordings confirm the feasibility of the proposed method.

Exploring the gray correlation analysis of student management and ideological and political education in colleges and universities

Abstract This paper takes grey correlation analysis as the theoretical basis and improves the original grey proximity correlation calculation method by adding bias terms. Uniformly select multiple sample datasets so that the proportion of positive and negative samples is roughly equal. Use the BiasGA method to calculate the biased grey correlation between users and other users to obtain the biased grey correlation matrix of users. Determine if there is a link between student management and ideological and political education by setting the correlation threshold. Integration of teaching cases and feasibility analysis led to the results being found. Teachers under 35 account for 68.75% of the total proportion. The teacher team tends to be younger, and they lack experience in ideological and political management. In student management, only a small number of teachers can improve the effectiveness of student management with the guidance of ideological and political education. the performance evaluation values of the five teachers were 72, 63, 72, 58, 61, respectively. The teachers did not do a comprehensive job of integrating the concept of ideological and political education into the management of students in colleges and universities. Daily affairs management should be strengthened in managing students in colleges and universities, and the integration of ideological and political education concepts should be emphasised.

Metric-Independent Mitigation of Unpredefined Bias in Machine Classification

In this study, we evaluate the “propose-review” framework for the mitigation of bias in machine classification. The framework considers Bob, who aims to protect sensitive dimensions from discrimination, and Alice, who sends proposals to Bob for using his data to construct a target classifier. The goal is to minimize discrimination in Bob’s protected dimension while preserving the desired separating capability of Alice’s classifier. The method does not assume predefined bias terms, does not anchor on specific fairness metrics, and is independent of Alice’s classifier choice. We consider that data attributes have different concentrations of the latent bias axes; assessing attributes’ concentrations in the ruled bias hyperspace helps identify bias-prone attributes and inform bias-mitigating data transforms. To this end, we assess attributes’ contribution to the separating capability of Bob’s conceptual classifier. We then compute the pairwise distances between attributes, and by applying multidimensional scaling to the distance matrix, we infer the axes of bias and establish a bias-attribute mapping. Bias mitigation is achieved by greedily applying appropriate data transforms to bias-prone attributes. The method works desirably across 21 classifiers and 7 datasets, bringing about substantial bias reduction under different choices of the protected dimension and the fairness metric. Compared to adversarial debiasing, the method better exploits the fairness-utility trade-off in machine classification.

Analytical Prescribed Performance Guidance with Field-of-View and Impact-Angle Constraints

Aiming at the impact-angle-control guidance with field-of-view constraint in the three-dimensional space, a novel analytical prescribed performance guidance law is proposed. First, a new prescribed performance function is designed in the relative range domain, and the common drawbacks of most existing prescribed performance functions, such as large overshoot, time dependency, and infinite-time convergence, are avoided. Second, the three-dimensional biased pure proportional navigation guidance frame is selected. The biased term is derived based on the prescribed performance control theory and the optimal error dynamic method, for realizing the terminal impact-angle constraint. After that, the analytical solution of missile lead angle is derived under some linearization approximations, and two methods are respectively developed for obtaining the range of the guidance gain and updating its value to guarantee the satisfaction of the field-of-view constraint during the guidance process. Moreover, for the proposed guidance law and methods, the tracking error could be ensured to remain in the predefined performance boundaries. Finally, numerical simulations are conducted to verify the effectiveness of the new theoretical findings.

Exploring the equilibrium and dynamic phase transition properties of the Ising ferromagnet on a decorated triangular lattice.

We study the equilibrium and dynamic phase transition properties of a two-dimensional Ising model on a decorated triangular lattice under the influence of a time-dependent magnetic field composed of a periodic square wave part plus a time-independent bias term. Using Monte Carlo simulations with a standard Metropolis algorithm, we determine the equilibrium critical behavior in zero field. At a fixed temperature corresponding to the multidroplet regime, we locate the relaxation time and the dynamic critical half period at which a dynamic phase transition takes place between ferromagnetic and paramagnetic states. Benefiting from finite-size scaling theory, we estimate the dynamic critical exponent ratios for the dynamic order parameter and its scaled variance, respectively. The response function of the average energy is found to follow a logarithmic scaling as a function of lattice size. At the critical half period and in the vicinity of a small bias field regime, the average of the dynamic order parameter obeys a scaling relation with a dynamic scaling exponent which is very close to the equilibrium critical isotherm value. Finally, in the slow critical dynamics regime, investigation of metamagnetic fluctuations in the presence of bias field reveals a symmetric double-peak behavior for the scaled variance contours of the dynamic order parameter and average energy. Our results strongly resemble those previously reported for kinetic Ising models.

Controlling qubit-oscillator systems using linear parameter sweeps

We investigate the dynamics of a qubit-oscillator system under the influence of a linear sweep of system parameters. We consider two main cases. In the first case, we consider sweeping the parameters between the regime of a weakly correlated ground state and the regime of a strongly correlated ground state, a situation that can be viewed as a finite-duration quench between two phases of matter: the normal phase and the superradiant phase. Excitations are created as a result of this quench. We investigate the dependence of the excitation probabilities on the various parameters. We find a qualitative asymmetry in the dynamics between the cases of a normal-to-superradiant and superradiant-to-normal quench. The second case of parameter sweeps that we investigate is the problem of a Landau–Zener sweep in the qubit bias term for a qubit coupled to a harmonic oscillator. We analyze a theoretical formula based on the assumption that the dynamics can be decomposed into a sequence of independent Landau–Zener transitions. In addition to establishing the conditions of validity for the theoretical formula, we find that under suitable conditions, deterministic and robust multi-photon state preparation is possible in this system.

Robust Kramers-Kronig holographic imaging with Hilbert-Huang transform.

Holography based on Kramers-Kronig relations (KKR) is a promising technique due to its high-space-bandwidth product. However, the absence of an iterative process limits its noise robustness, primarily stemming from the lack of a regularization constraint. This Letter reports a generalized framework aimed at enhancing the noise robustness of KKR holography. Our proposal involves employing the Hilbert-Huang transform to connect the real and imaginary parts of an analytic function. The real part is initially processed by bidimensional empirical mode decomposition into a series of intrinsic mode functions (IMFs) and a residual term. They are then selected to remove the noise and bias terms. Finally, the imaginary part can be obtained using the Hilbert transform. In this way, we efficiently suppress the noise in the synthetic complex function, facilitating high-fidelity wavefront reconstruction using ∼20% of the exposure time required by existing methods. Our work is expected to expand the applications of KKR holography, particularly in low phototoxicity biological imaging and other related scenarios.

Application of a Stochastic Gradient Descent-Based SVR Online Modeling Method to Time-Varying Forging Processes

In practice, forging processes have many unknown dynamics and fast time-varying characteristics due to transient load variation. This often results in insufficient data samples. Usually, support vector regression (SVR) can accurately model small sample data due to its good sparsity. However, using of sequential minimum optimization (SMO) algorithms increases the computational costs during the solution process, making it difficult to model fast time-varying systems. Aiming to address this problem, we developed an approach using online stochastic gradient descent (SGD) to improve SVR modeling efficiency. First, we used loss coefficients derived from the loss function to represent support vector loss. This effectively ensured sparsity in the modeling process. In this way, the SVR solving process was transformed into a loss coefficient calculation. This calculation was easy to achieve using SGD; thus, the solving process complexity was greatly reduced compared to SMO. On this basis, we developed an online incremental strategy to adapt the time-varying dynamics using online updating of step length, loss coefficients and bias term. Additional analysis demonstrated the convergence of the proposed online modeling method. Furthermore, modeling effect of this method is verified by using actual experiments with a 40MN isothermal die forging press.

-Stable convergence of heavy-/light-tailed infinitely wide neural networks

AbstractWe consider infinitely wide multi-layer perceptrons (MLPs) which are limits of standard deep feed-forward neural networks. We assume that, for each layer, the weights of an MLP are initialized with independent and identically distributed (i.i.d.) samples from either a light-tailed (finite-variance) or a heavy-tailed distribution in the domain of attraction of a symmetric $\alpha$ -stable distribution, where $\alpha\in(0,2]$ may depend on the layer. For the bias terms of the layer, we assume i.i.d. initializations with a symmetric $\alpha$ -stable distribution having the same $\alpha$ parameter as that layer. Non-stable heavy-tailed weight distributions are important since they have been empirically seen to emerge in trained deep neural nets such as the ResNet and VGG series, and proven to naturally arise via stochastic gradient descent. The introduction of heavy-tailed weights broadens the class of priors in Bayesian neural networks. In this work we extend a recent result of Favaro, Fortini, and Peluchetti (2020) to show that the vector of pre-activation values at all nodes of a given hidden layer converges in the limit, under a suitable scaling, to a vector of i.i.d. random variables with symmetric $\alpha$ -stable distributions, $\alpha\in(0,2]$ .

Three-Dimensional Spatial–Temporal Cooperative Guidance Without Active Speed Control

This paper investigates the three-dimensional (3D) spatial–temporal cooperative guidance problem for multiple missiles with time-varying speeds, which is addressed by a progressive design strategy. First, the zero miss distance and desired impact directions are realized by a 3D vector impact angle control guidance (IACG) law whose time-to-go is predicted accurately and efficiently by a numerical algorithm. Then, by introducing an implicit trajectory-to-go function, the time-to-go control mechanism is clearly revealed under missile speed variation. Accordingly, a coordinated biased term is added to the 3D vector IACG law to ensure a simultaneous attack. Finally, considering the seeker’s field-of-view (FOV) limit, the resultant cooperative guidance law is further augmented by another biased term to prevent the lead angle from exceeding its upper bound. Unlike similar existing results, the proposed one can satisfy multiple complex spatial–temporal constraints without active speed control, thereby manifesting great practical significance. Several numerical simulations are provided to show the effectiveness and advantages of the proposed cooperative guidance law.

Improved seismic envelope full-waveform inversion

The envelope full-waveform-inversion method has the potential to greatly improve the estimation of long-wavelength components of subsurface seismic velocity, and simultaneously avoid or reduce inversion cycle skipping, by fitting the envelopes of observed and modeled seismic waveform data, rather than fitting the waveforms directly. However, some issues such as instability of the adjoint source and gradient term make it challenging to use in practice for many seismology applications. We examine the envelope inversion (EI) methods with different envelope function exponent p values from an adjoint source analysis perspective and reveal the adverse effects of the adjoint source weighting term on the data residuals, which degrade or mute important inversion gradient contributions, especially from reflected or scattered phases in the seismic data. We develop the theory and implementation for an improved EI (IEI) method by adding an upshift constant (zero-frequency direct current [DC] bias term) to the seismic waveform data before calculating the envelope functions, which can help solve the instability issue of the adjoint source of the EI with power value p = 1. Our approach is different than the common but simple addition of a “water-level” damping constant in the denominator of the data residual-weighting term, which can ignore important information contained in the data residuals. Our IEI method becomes a mixed envelope-waveform objective function, which introduces a new adjoint source weighting term that can preserve all of the information contained in the data residuals, by boosting the low-amplitude phases of the seismic waveform, while simultaneously preserving the correct envelope (energy shape) of the high-amplitude phases. We test the performance of all three methods in detail using the highly realistic SEAM4D model and data and find the advantages of our new IEI method compared with conventional EI and full-waveform inversion methods.

A borehole array data–based approach for conducting 1D site response analyses II: Accounting for modeling errors

Site response estimates from one-dimensional (1D) site response analyses (SRAs) carry inaccuracies due to modeling and parametric errors. Modeling errors are due to the condensation of the three-dimensional (3D) wave propagation phenomenon to the vertical propagation of a horizontally polarized wave through a soil column, and parametric errors are due to the incomplete knowledge of the distributions of soil parameters, leading to the selection of nonoptimal input parameters for a site of interest. While parametric errors are traditionally handled using different soil parameters (e.g. alternative shear-wave velocity profiles), modeling errors are generally neglected. This paper proposes an approach for conducting linear elastic 1D SRAs to improve site response predictions and account for modeling errors. First, ground-motion data from borehole array sites are collected, processed, and screened for appropriateness (e.g. expected shear strains lower than 0.01%, signal-to-noise ratio higher than 3). Second, 1D SRA predictions in terms of transfer functions and amplification factors are compared against observations, and the discrepancies are quantified as residuals. Finally, the residuals are partitioned into a model bias term [Formula: see text], a site term [Formula: see text] with standard deviation [Formula: see text], and a event- and site-specific remaining residual [Formula: see text] with standard deviation [Formula: see text]. Values for [Formula: see text] and [Formula: see text] for forward predictions are recommended. The sensitivity of the site response residuals to region, site type (1D- or 3D-like), and the applicability of findings to outcropping applications are discussed, and an example application for a hypothetical project site is presented.

An Adaptive Soft Sensor for On‐Line Monitoring the Mass Conversion in the Emulsion Copolymerization of the Continuous SBR Process

AbstractSoft sensors (SS) are of importance in monitoring polymerization processes because numerous production and quality variables cannot be measured online. Adaptive SSs are of interest to maintain accurate estimations under disturbances and changes in operating points. This study proposes an adaptive SS to online estimate the mass conversion in the emulsion copolymerization required for the production of Styrene‐Butadiene rubber (SBR). The SS includes a bias term calculated from sporadic laboratory measurements. Typically, the bias is updated every time a new laboratory report becomes available, but this strategy leads to unnecessarily frequent bias updates. The SS includes a statistic‐based tool to avoid unnecessary bias updates and reduce the variability of the bias with respect to classical approaches. A control chart (CC) for individual determinations combined with an algorithmic Cusum is used to monitor the statistical stability of the average prediction error. The adaptive SS enables a bias update only when a loss of said statistical stability is detected. Several bias update methods are tested on a simulated industrial train of reactors for the latex production in the SBR process. The best results are obtained by combining the proposed CC‐based approach with a previously developed Bayesian bias update strategy.

Three-dimensional impact-time-constrained proportional navigation guidance using range-varying gain

This paper presents a novel feedback guidance law to address the impact time control problem of homing missiles in three-dimensional (3D) space. First, a range-varying gain is incorporated into the conventional proportional navigation guidance (PNG) law, which leads to globally precise time-to-go solution and achievable impact time region. Next, an impact time control guidance (ITCG) law is developed by augmenting the proposed varying-gain PNG law with a feedback biased term. Unlike most existing studies, the proposed ITCG law does not involve any linearized approximations or numerical iterative schemes. Furthermore, it can achieve the desired impact time and high-precision interception for a wide launch envelop with arbitrary initial leading angles. Finally, extensive numerical simulations, including comparison studies and Monte Carlo assessments, are carried out to validate the theoretical findings.

Enhanced design of multiplexed coded masks for Fresnel incoherent correlation holography

Fresnel incoherent correlation holography (FINCH) is a well-established incoherent digital holography technique. In FINCH, light from an object point splits into two, differently modulated using two diffractive lenses with different focal distances and interfered to form a self-interference hologram. The hologram numerically back propagates to reconstruct the image of the object at different depths. FINCH, in the inline configuration, requires at least three camera shots with different phase shifts between the two interfering beams followed by superposition to obtain a complex hologram that can be used to reconstruct an object’s image without the twin image and bias terms. In general, FINCH is implemented using an active device, such as a spatial light modulator, to display the diffractive lenses. The first version of FINCH used a phase mask generated by random multiplexing of two diffractive lenses, which resulted in high reconstruction noise. Therefore, a polarization multiplexing method was later developed to suppress the reconstruction noise at the expense of some power loss. In this study, a novel computational algorithm based on the Gerchberg-Saxton algorithm (GSA) called transport of amplitude into phase (TAP-GSA) was developed for FINCH to design multiplexed phase masks with high light throughput and low reconstruction noise. The simulation and optical experiments demonstrate a power efficiency improvement of ~ 150 and ~ 200% in the new method in comparison to random multiplexing and polarization multiplexing, respectively. The SNR of the proposed method is better than that of random multiplexing in all tested cases but lower than that of the polarization multiplexing method.

Hierarchical network meta-analysis models for synthesis of evidence from randomised and non-randomised studies

BackgroundWith the increased interest in the inclusion of non-randomised data in network meta-analyses (NMAs) of randomised controlled trials (RCTs), analysts need to consider the implications of the differences in study designs as such data can be prone to increased bias due to the lack of randomisation and unmeasured confounding. This study aims to explore and extend a number of NMA models that account for the differences in the study designs, assessing their impact on the effect estimates and uncertainty.MethodsBayesian random-effects meta-analytic models, including naïve pooling and hierarchical models differentiating between the study designs, were extended to allow for the treatment class effect and accounting for bias, with further extensions allowing for bias terms to vary depending on the treatment class. Models were applied to an illustrative example in type 2 diabetes; using data from a systematic review of RCTs and non-randomised studies of two classes of glucose-lowering medications: sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 receptor agonists.ResultsAcross all methods, the estimated mean differences in glycated haemoglobin after 24 and 52 weeks remained similar with the inclusion of observational data. The uncertainty around these estimates reduced when conducting naïve pooling, compared to NMA of RCT data alone, and remained similar when applying hierarchical model allowing for class effect. However, the uncertainty around these effect estimates increased when fitting hierarchical models allowing for the differences in study design. The impact on uncertainty varied between treatments when applying the bias adjustment models. Hierarchical models and bias adjustment models all provided a better fit in comparison to the naïve-pooling method.ConclusionsHierarchical and bias adjustment NMA models accounting for study design may be more appropriate when conducting a NMA of RCTs and observational studies. The degree of uncertainty around the effectiveness estimates varied depending on the method but use of hierarchical models accounting for the study design resulted in increased uncertainty. Inclusion of non-randomised data may, however, result in inferences that are more generalisable and the models accounting for the differences in the study design allow for more detailed and appropriate modelling of complex data, preventing overly optimistic conclusions.

Cooperative Circular Guidance of Multiple Missiles: A Practical Prescribed-Time Consensus Approach

This paper develops a new class of distributed cooperative guidance laws under both undirected and directed communication topologies based on the circular guidance and the practical prescribed-time consensus. The circular guidance law is augmented with a bias term to achieve impact time synchronization within a prescribed time. The analytical time-to-go expression under the circular guidance law is utilized to ensure the impact time cooperation accuracy. Not only convergence rate but also acceleration magnitude reduction is guaranteed due to the merits of the practical prescribed-time consensus. Moreover, based on the predicted interception point, the proposed guidance laws are extended to a nonmaneuvering target. Finally, numerical simulations in both two-dimensional and three-dimensional scenarios verify the effectiveness, robustness, and superiority of the proposed guidance laws.