Fixed Model Parameters Research Articles

Estimation of AUV Hydrodynamic Coefficients Using Analytical and System Identification Approaches

This article discusses a challenge of the hydrodynamic coefficient identification in autonomous underwater vehicles (AUVs). The considered coefficients correspond to a nonlinear mathematical model of an AUV maneuvering in the horizontal plane. The dynamics of underwater vehicles is intrinsically nonlinear. Hence, to reproduce different types of maneuvers, such as straight line, zigzag, or turning in circles, a nonlinear mathematical model is important. The proposed identification approach combines an analytical and semiempirical estimation method (ASE), based on the hydrodynamics and geometric characteristics of submarine vehicles, with a parameter estimator based on the extended Kalman filter. The identification starts with ASE estimations, and then the estimator based on the extended Kalman filter adjusts the hydrodynamic coefficient estimates based on the experimental data. The experimental data were obtained with the sensors of the Pirajuba AUV during sea trials. This identification method is a cyclical estimation process, where the less reliable parameters are adjusted to the experimental data while the most reliable parameters are kept fixed. Therefore, in the next identification iteration, the originally fixed model parameters are adjusted to the experimental data, keeping the already identified values. Finally, the identified model is used to simulate the vehicle's movement, and the movement variables are compared to the experimental data, thus validating the identified model.

Integrating Acoustic Telemetry into a Mark–Recapture Model to Improve Catchability Parameters and Abundance Estimates of Lake Sturgeon in Eastern Lake Erie

AbstractEstimating abundances of rare fish species that exhibit low recapture rates can be challenging and can require decades’ worth of data to produce reliable results. However, when data sets are limited, auxiliary information used to fix model parameters may improve model outputs if that information is reasonably estimated. We collected mark–recapture data on 195 unique Lake Sturgeon Acipenser fulvescens in eastern Lake Erie near the headwaters of the Niagara River between 2012 and 2018. We ran Jolly–Seber capture–recapture models with the POPAN formulation in Program R using the RMark package to estimate abundance within our study area. Initial model runs produced unreasonable parameters (e.g., >99% survival). To improve our model, we integrated acoustic telemetry presence–absence data to calculate and fix catchability parameters in our model. Integrating acoustic telemetry‐derived catchability parameters in our mark–recapture model produced adult abundance estimates (mean ± SE = 889 ± 184) that might not otherwise have been achieved given our relatively small data set and few recaptures. Our findings demonstrate the potential value of integrating reasonably estimated parameters generated by acoustic telemetry studies into mark–recapture.

One-dimensional turbulence modelling of incompressible temporally developing turbulent boundary layers with comparison to DNS

ABSTRACTThe incompressible temporally developing turbulent boundary layer (TBL) is analysed using the map-based stochastic one-dimensional turbulence (ODT) model. The TBL is a canonical flow problem, which is, in the present study, formed by a planar moving wall and a free stream at rest. An understanding of this idealised flow is of fundamental relevance for the numerical analysis of turbulent boundary-layer-type flows. In the present ODT simulations, the flow variables are resolved on all scales along a wall-normal, one-dimensional domain. These variables are evolved by a deterministic and a stochastic process. The latter models the effect of turbulent advection and pressure fluctuations, whereas the former represents molecular diffusion. The model is appropriate for high Reynolds numbers for which the turbulence field exhibits a broad range of scales and is notionally featureless. We show that ODT is able to capture salient features of the TBL by comparing the various statistics with available reference direct numerical simulation (DNS) results for different bulk Reynolds numbers in the range using fixed model parameters. The influence of the model parameters is analysed for and optimal parameter values are provided. The results discussed in this paper suggest that ODT is an economical and reasonably accurate approach for the simulation of transient turbulent boundary-layer-type flows.

Small-scale resolving simulations of the turbulent mixing in confined planar jets using one-dimensional turbulence

Small-scale effects of turbulent mixing are numerically investigated by applying the map-based, stochastic, one-dimensional turbulence (ODT) model to confined planar jets. The model validation is carried out for the momentum transport by comparing ODT results to available reference data for the bulk Reynolds numbers Re=20,000 and 40,000. Various pointwise statistical quantities are computed and compared to the available reference data. We show that these quantities can be captured well, or at least to a reasonable extent, by the stand-alone model formulation and for fixed model parameters. Only the root-mean-square velocity fluctuations remain systematically underestimated in the ODT results (by an approximate factor of 1.5). Afterwards, the turbulent transport of a passive scalar is addressed for the Schmidt numbers Sc=1 and 1250. For the high Schmidt number and in contrast to the velocity fluctuations, it is shown that the scalar fluctuation variance is up to ten times larger in the ODT simulations resolving the Batchelor scale. The fluctuation variance is notably smaller for the lower Schmidt number, but exhibits better agreement with the references at a nominally higher Schmidt number. We suggest that this is due to implicit filtering in the references, which barely resolve the Kolmogorov scale. ODT turbulence spectra support this interpretation since a Batchelor-like scalar turbulence spectrum is only observed for the higher Schmidt number. With the aid of these spectra and the fluctuation statistics we conclude that implicit filtering has a similar effect as a reduction of the Schmidt number.

Joint Maximum Likelihood Estimation for High-Dimensional Exploratory Item Factor Analysis.

Joint maximum likelihood (JML) estimation is one of the earliest approaches to fitting item response theory (IRT) models. This procedure treats both the item and person parameters as unknown but fixed model parameters and estimates them simultaneously by solving an optimization problem. However, the JML estimator is known to be asymptotically inconsistent for many IRT models, when the sample size goes to infinity and the number of items keeps fixed. Consequently, in the psychometrics literature, this estimator is less preferred to the marginal maximum likelihood (MML) estimator. In this paper, we re-investigate the JML estimator for high-dimensional exploratory item factor analysis, from both statistical and computational perspectives. In particular, we establish a notion of statistical consistency for a constrained JML estimator, under an asymptotic setting that both the numbers of items and people grow to infinity and that many responses may be missing. A parallel computing algorithm is proposed for this estimator that can scale to very large datasets. Via simulation studies, we show that when the dimensionality is high, the proposed estimator yields similar or even better results than those from the MML estimator, but can be obtained computationally much more efficiently. An illustrative real data example is provided based on the revised version of Eysenck's Personality Questionnaire (EPQ-R).

No empirical evidence to support the hypothesis that daily climate variation has an effect on species’ elevational range size: Reply to Chan et al.

AbstractFollowing our critique, Chan et al. defend the approach used in their original paper. They reveal their “iterative strategy of SEM” (structural equation modelling), which they claim is “standard” (we show otherwise) and “required for the proper and most effective use of SEM” for hypothesis testing. However, publishing their detailed procedure exposes fundamental flaws: capitalizing on chance and violating important assumptions and principles of SEM. They used the same data to first explore numerous correlations, then fit 29 candidate models (all failed) using the best correlates, then fix model parameters to gain degrees of freedom, then evaluate the “best” model. In producing the “best” model, they fixed five parameters using estimates from regression on the same dataset. They further argue that their stationary bootstrap cures the problems of bias and pseudoreplication; we disagree. At best, Chan et al. developed a hypothesis; they did not perform a valid test of one.

Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation

Real-time crash risk evaluation is an emerging analysis topic which can identify crash-prone traffic conditions and further benefit the implementation of the active traffic safety management systems on urban expressways. Studies have been conducted to develop crash risk evaluation models with different modeling techniques. However, they were developed and calibrated with fixed model parameters, which cannot account for different traffic patterns with the spatial-temporal variability, and the on-line model updating issue with in-field streaming traffic data has not been well analyzed. In this study, the Bayesian dynamic logistic regression (Bayesian Dynamic LR) was introduced to develop the real-time crash risk evaluation model, where the model parameters could dynamically change by efficiently integrating a new instance with prior knowledge when it arrives. Firstly, a Bayesian Dynamic LR model was developed and compared with a standard logistic regression (Standard LR) model and four support vector machine (SVM) models with different kernel functions for the prediction accuracy evaluated by the Area under the Receiver Operating Characteristics curve (AUC). Then, the sensitivity analysis of the Bayesian Dynamic LR model was conducted on its two key parameters, which are forgetting parameter λ and the ratio of crash to non-crash observations. The analyses results concluded as (i) Bayesian Dynamic LR model has by far the best predictive performance, where AUC could reach around 0.987 which is also the very highest in existing related literatures to the best of our knowledge; (ii) using a less informative prior in the updating equation stage, makes the model performance better; (iii) when the crash to non-crash ratio increases from 1:1 to 1:10, the model performance is gradually enhanced and it tends to be stable at the AUC level of 0.971. Finally, an on-line updating modeling framework was proposed from the model implementation perspective and its prediction performance was proved by a full set in real-world, and the implementation challenges in real-world was discussed.

An Integrated Prognostics Approach for Pipeline Fatigue Crack Growth Prediction Utilizing Inline Inspection Data

Fatigue cracking is a key type of defect for liquid pipelines, and managing fatigue cracks has been a top priority and a big challenge for liquid pipeline operators. The existing inline inspection (ILI) tools for pipeline defect evaluation have large fatigue crack measurement uncertainties. Furthermore, the current physics-based methods are mainly used for fatigue crack growth prediction, where the same or a small range of fixed model parameters is used for all pipes. They result in uncertainty that is managed through the use of conservative safety factors such as adding depth uncertainty to the measured depth in deciding integrity management and risk mitigation strategies. In this study, an integrated approach is proposed for pipeline fatigue crack growth prediction utilizing ILI data including consideration of crack depth measurement uncertainty. This approach is done by integrating the physical models, including the stress analysis models, the crack growth model governed by the Paris’ law, and the ILI data. With the proposed integrated approach, the finite element (FE) model of a cracked pipe is built and the stress analysis is performed. ILI data are utilized to update the uncertain physical parameters for the individual pipe being considered so that a more accurate fatigue crack growth prediction can be achieved. Time-varying loading conditions are considered in the proposed integrated method by using rainflow counting method. The proposed integrated prognostics approach is compared with the existing physics-based method using examples based on simulated data. Field data provided by a Canadian pipeline operator are also employed for the validation of the proposed method. The examples and case studies in this paper demonstrate the limitations of the existing physics-based method, and the promise of the proposed method for achieving accurate fatigue crack growth prediction as continuous improvement of ILI technologies further reduces ILI measurement uncertainty.

Empirical Bayesian Light-Field Stereo Matching by Robust Pseudo Random Field Modeling.

Light-field stereo matching problems are commonly modeled by Markov Random Fields (MRFs) for statistical inference of depth maps. Nevertheless, most previous approaches did not adapt to image statistics but instead adopted fixed model parameters. They explored explicit vision cues, such as depth consistency and occlusion, to provide local adaptability and enhance depth quality. However, such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense view sampling are not suitable for sparse one. In this paper, we get back to MRF fundamentals and develop an empirical Bayesian framework-Robust Pseudo Random Field-to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihoods with hidden soft-decision priors, we apply soft expectation-maximization (EM) for good model fitting and perform hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. Accordingly, we devise a stereo matching algorithm to employ this framework on dense, sparse, and even denoised light fields. It can be applied to both true-color and grey-scale pixels. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.

Option price and market instability

An option pricing formula, for which the price of an option depends on both the value of the underlying security as well as the velocity of the security, has been proposed in Baaquie and Yang (2014). The FX (foreign exchange) options price was empirically studied in Baaquie et al., (2014), and it was found that the model in general provides an excellent fit for all strike prices with a fixed model parameters—unlike the Black–Scholes option price Hull and White (1987) that requires the empirically determined implied volatility surface to fit the option data. The option price proposed in Baaquie and Cao Yang (2014) did not fit the data during the crisis of 2007–2008. We make a hypothesis that the failure of the option price to fit data is an indication of the market’s large deviation from its near equilibrium behavior due to the market’s instability. Furthermore, our indicator of market’s instability is shown to be more accurate than the option’s observed volatility. The market prices of the FX option for various currencies are studied in the light of our hypothesis.

Parameters Optimization of Lebesgue Sampling-based Fault Diagnosis and Prognosis with Application to Li-ion Batteries

Lebesgue sampling-based fault diagnosis and prognosis (LSFDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the diagnostic and prognostic models. The predicted results will show great discrepancy with the real remaining useful life (RUL) in applications if the model is not accurate. In addition, the fixed model parameters cannot accommodate the varying stress factors that affect the fault dynamics. To address this problem, the parameters in the models are treated as time-varying ones and are adjusted online to accommodate changing dynamics. In this paper, a recursive least square (RLS) based method with a forgetting factor is employed to make the diagnostic and prognostic models online adaptive in LS-FDP. The design and implementation of LS-FDP are based on a particle filtering algorithm and are illustrated with experiments of Li-ion batteries. The experimental results show that the performance of LS-FDP with model adaptation is improved on both battery capacity estimation and RUL prediction.

Combined Parameter and State Estimation Problem in a Complex Domain: RF Hyperthermia Treatment Using Nanoparticles

The particle filter methods have been widely used to solve inverse problems with sequential Bayesian inference in dynamic models, simultaneously estimating sequential state variables and fixed model parameters. This methods are an approximation of sequences of probability distributions of interest, that using a large set of random samples, with presence uncertainties in the model, measurements and parameters. In this paper the main focus is the solution combined parameters and state estimation in the radiofrequency hyperthermia with nanoparticles in a complex domain. This domain contains different tissues like muscle, pancreas, lungs, small intestine and a tumor which is loaded iron oxide nanoparticles. The results indicated that excellent agreements between estimated and exact value are obtained.

Nonlinear longitudinal attitude control of an unmanned seaplane with wave filtering

A new longitudinal attitude control system design for an unmanned seaplane in the severe sea states is presented in this paper. We develop a nonlinear passive observer, which is used to achieve wave filtering and state estimation. Moreover, the observer can be extended to achieve adaptive wave filtering in varying sea states. Using the estimated low-frequency states, a backstepping sliding mode controller is designed to keep the longitudinal attitude of the unmanned seaplane stable. The stability of the total closed loop system is analyzed by using Lyapunov theory. Simulations are performed in different wave conditions, including Seastate 3 and Seastate 5. The simulations results show that the proposed longitudinal attitude controller can improve the anti-waves capability effectively. Moreover, adaptive wave filter has a significant advantage over a filter with fixed model parameters in varying sea states.

Central exclusive diffractive production of theπ+π−continuum, scalar, and tensor resonances inppandpp¯scattering within the tensor Pomeron approach

We consider central exclusive diffractive dipion production in the reactions $pp \to pp \pi^{+} \pi^{-}$ and $p\bar{p} \to p\bar{p} \pi^{+} \pi^{-}$ at high energies. We include the dipion continuum, the dominant scalar $f_{0}(500)$, $f_{0}(980)$, and tensor $f_{2}(1270)$ resonances decaying into the $\pi^{+} \pi^{-}$ pairs. The calculation is based on a tensor pomeron model and the amplitudes for the processes are formulated in terms of vertices respecting the standard crossing and charge-conjugation relations of Quantum Field Theory. The formulae for the dipion continuum and tensor meson production are given here for the first time. The theoretical results are compared with existing STAR, CDF, CMS experimental data and predictions for planned or being carried out experiments (ALICE, ATLAS) are presented. We show the influence of the experimental cuts on the integrated cross section and on various differential distributions for outgoing particles. Distributions in rapidities and transverse momenta of outgoing protons and pions as well as correlations in azimuthal angle between them are presented. We find that the relative contribution of resonant $f_2(1270)$ and dipion continuum strongly depends on the cut on proton transverse momenta or four-momentum transfer squared $t_{1,2}$ which may explain some controversial observations made by different ISR experiments in the past. The cuts may play then the role of a $\pi \pi$ resonance filter. We suggest some experimental analyses to fix model parameters related to the pomeron-pomeron-$f_{2}$ coupling.

On Heuristic Computation and Application of Flexibility Indices for Unsteady Process Design

The unsteady chemical processes, designed according to the nominal operating conditions and fixed model parameters, have traditionally been evaluated with economic criteria. This design strategy often ends up with a plant which may become inoperable if some of the process conditions/parameters unexpectedly deviate from their normal levels. Two alternative performance measures, the dynamic and temporal flexibility indices, have thus been devised in recent years to characterize designs from a different viewpoint. However, not only their significances still have not been adequately interpreted and their roles clarified, but also their numerical values cannot be determined efficiently with the available algorithms. In this study, the simple trapezoidal rule is applied to discretize and integrate the dynamic models and a heuristic strategy is then utilized to implement the vertex method for computing the aforementioned metrics efficiently. To demonstrate the effectiveness of the proposed computation approaches...

Exclusive diffractive production ofπ+π−continuum and resonances within tensor pomeron approach

We discuss exclusive central diffractive dipion production in proton-(anti)proton collisions at high energies. The calculation is based on a tensor pomeron model and the amplitudes for the processes are formulated in an effective field-theoretic approach. We include the purely diffractive dipion continuum, and the scalar and tensor resonances decaying into the π + π − pairs as well as the photoproduction contributions ( ρ 0 , Drell-Soding). We discuss how two pomerons couple to tensor meson f 2 (1270) and the interference effects of resonance and dipion continuum. The theoretical results are compared with CDF and CMS experimental data. We show the influence of the experimental cuts on the integrated cross section and on various differential distributions for outgoing particles. We find that the relative contribution of resonant f 2 (1270) and dipion continuum strongly depends on the cut on proton transverse momenta (or four-momentum transfer squared t 1,2 ) which may explain some controversial observations made by different ISR experiments in the past. The cuts may play then the role of a ππ resonance filter. We suggest some experimental analyses to fix model parameters related to the pomeron-pomeron-meson coupling.

Evaluating the cooling and computing energy demand of a datacentre with optimal server provisioning

As cloud computing continues to gain significance across fields, the energy consumption of datacentres creates new challenges in the design and operation of computer systems, with cooling remaining a key part of the total energy expenditure. We investigate the implications of increasing the room temperature setpoint in datacentres to save energy. For this, we develop a holistic model for the energy consumption of the server room that depends on user workload and service level agreement constraints, and that considers both cooling and computing energy dissipation. The model is applicable to a steady-state analysis of the system and brings insight into the impact of the most relevant parameters that affect the net energy consumption, such as the outside temperature, room temperature setpoint, and user demand. We analyse both static and dynamic server provisioning cases. In the latter case, a global power management scheme determines the optimal number of servers required to handle the incoming user demand to fulfil a target service level objective. Finally, we consider the extra energy needed to maintain service continuity under the expected higher server mortality rate due to warmer operational temperatures. Energy and temperature measurements acquired from a server machine running scientific benchmark programs allow to realistically fix model parameters for the study and to obtain pragmatic conclusions.

Field equation of the correlation function of mass-density fluctuations for self-gravitating systems

We study the mass density distribution of Newtonian self-gravitating systems. Modeling the system as a fluid in hydrostatical equilibrium, we obtain from first principle the field equation and its solution of correlation function $\xi(r)$ of the mass density fluctuation itself. We apply thid to studies of the large-scale structure of the Universe within a small redshift range. The equation tells that $\xi(r)$ depends on the point mass $m$ and the Jeans wavelength scale $\lambda_{0}$, which are different for galaxies and clusters. It explains several longstanding, prominent features of the observed clustering : that the profile of $\xi_{cc}(r)$ of clusters is similar to $\xi_{gg}(r)$ of galaxies but with a higher amplitude and a longer correlation length, and that the correlation length increases with the mean separation between clusters as a universal scaling $r_0\simeq 0.4d$. Our solution $\xi(r)$ also yields the observed power-law correlation function of galaxies $\xi_{gg}(r)\simeq (r_0/r)^{1.7}$ valid only in a range $1<r<10 h^{-1}$Mpc. At larger scales the solution $\xi(r)$ breaks below the power law and goes to zero around $\sim 50h^{-1}$Mpc, just as the observational data have demonstrated. With a set of fixed model parameters, the solutions $\xi_{gg}(r)$ for galaxies, the corresponding power spectrum, and $\xi_{cc}(r)$ for clusters, simultaneously, agree with the observational data from the major surveys of galaxies, and of clusters.

Degradation-based maintenance decision using stochastic filtering for systems under imperfect maintenance

The notion of imperfect maintenance has spawned a large body of literature, and many imperfect maintenance models have been developed. However, there is very little work on developing suitable imperfect maintenance models for systems outfitted with sensors. Motivated by the practical need of such imperfect maintenance models, the broad objective of this paper is to propose an imperfect maintenance model that is applicable to systems whose sensor information can be modeled by stochastic processes. The proposed imperfect maintenance model is founded on the intuition that maintenance actions will change the rate of deterioration of a system, and that each maintenance action should have a different degree of impact on the rate of deterioration. The corresponding parameter-estimation problem can be divided into two parts: the estimation of fixed model parameters and the estimation of the impact of each maintenance action on the rate of deterioration. The quasi-Monte Carlo method is utilized for estimating fixed model parameters, and the filtering technique is utilized for dynamically estimating the impact from each maintenance action. The competence and robustness of the developed methods are evidenced via simulated data, and the utility of the proposed imperfect maintenance model is revealed via a real data set.