Piecewise Modeling Research Articles

High-frequency sampling and piecewise models reshape dispersal kernels of a common reef coral.

Models of dispersal potential are required to predict connectivity between populations of sessile organisms. However, to date, such models do not allow for time-varying rates of acquisition and loss of competence to settle and metamorphose, and permit only a limited range of possible survivorship curves. We collect high-resolution observations of coral larval survival and metamorphosis, and apply a piecewise modeling approach that incorporates a broad range of temporally varying rates of mortality and loss of competence. Our analysis identified marked changes in competence loss and mortality rates, the timing of which implicates developmental failure and depletion of energy reserves. Asymmetric demographic rates suggest more intermediate-range dispersal, less local retention, and less long-distance dispersal than predicted by previously employed non-piecewise models. Because vital rates are likely temporally asymmetric, at least for nonfeeding broadcast-spawned larvae, piecewise analysis of demographic rates will likely yield more reliable predictions of dispersal potential.

A load-dependent PWA-H∞ controller for semi-active suspensions to exploit the performance of MR dampers

Magnetorheological (MR) dampers enable wide-range adjustment of damping and are considered as promising actuators for suspension performance optimization. However, MR dampers also exhibit complex nonlinearities, including hysteresis and bi-viscosity. Moreover, in addition to handling road irregularities, the suspension system needs to adjust its performance according to variations in vehicle load. The above nonlinearities and requirements both bring challenges to the design of semi-active suspension controllers. In this paper, the nonlinearities of MR dampers are handled with a piecewise modeling method, and the vibration attenuation is realized by designing a static piecewise-affine (PWA) H∞ controller. Furthermore, to deal with the time-varying load and to fully utilize the capability of MR dampers, a parameter-dependent piecewise-quadratic Lyapunov function (PDPQLF) is employed, which results in a load-dependent PWA (LDPWA) H∞ controller. Simulation and experimental results under random, bumpy and sinusoidal excitations show that the proposed controller can achieve enhancements on both comfort and handling performance. Above all, the proposed method enables effective high-frequency vibration suppression for underload conditions, while for overload conditions, the low-frequency performance is more significantly improved.

State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect

State-of-Health (SOH) is an intuitive reflection to the capacity during the degradation of lithium-ion batteries. Accurate SOH estimation can not only grasp the battery performance but also achieve a better balance between the safety and economic benefits for lithium-ion battery application system. Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during long rest time. This paper mainly studies the impact of relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel SOH estimation method based on the Wiener process. First, the life cycle of a lithium-ion battery is divided into three parts, i.e., the degradation process that eliminates the relaxation effect, the capacity regeneration process during the rest time, and the degradation process of the regenerated capacity. Next, the degradation model after eliminating the relaxation effect is established based on linear Wiener process, the capacity regenerated model is developed by the normal distribution, and the degradation model of regenerated capacity is established based on a nonlinear Wiener process. Then, for the degradation model based on nonlinear Wiener process, a two-step maximum likelihood estimation (MLE) method for prior parameters is proposed, and the random variables representing the personality features are updated online under the Bayesian framework. For the capacity regenerated model, a parameter estimation method based on MLE is proposed. In addition, a one-step and a multi-step SOH prediction method based on piecewise modeling are developed. Finally, the experiments are carried out based on the degradation data of lithium-ion batteries published by the NASA, and the results show that the method proposed in this paper can effectively improve the accuracy of the SOH estimation.

A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings

Slewing bearings are vital functional components of large machinery. It is of far reaching significance to study their life prediction and health management. Many studies are based on data-driven approaches. However, part of them in the form of “black-box” lack actual physical meanings due to opacity model structures and have difficulty in choosing optimal parameters. Few kinds of literature focus on explicit model relationships for slewing bearings' life models. In this paper, a novel approach based on symbolic regression is proposed with the aim of exploring slewing bearings' explicit life models in depth and to predict residual useful life (RUL). The proposed method integrates the strengths of multiple signals describing a comprehensive response to slewing bearings' health and various genetic programming (GP) algorithms modeling life expressions. In addition, independent, hybrid, and piecewise strategies are introduced and explicit model relationships with respect to degradation indicators (DIs) are established via GPs. To verify the proposed method, three run-to-failure experiments under discrepant operating conditions of slewing bearings are carried out. Prediction results demonstrate that models generated by epigenetic linear genetic programming (ELGP) under hybrid and piecewise modeling strategy with similarity-based combination strategy perform best. More importantly, their life expressions are more succinct and intelligible than in other situations.

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi‐Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real‐time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi‐Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low‐power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.

Modeling of sectionally continuous communication channel with inhomogeneously distributed tissues.

This study aimed to investigate effects on the transmission channel caused by heterogeneous distribution in tissues and joint characteristics. Human arm section scans were taken using CT technology, and zoned, following which, a circumference measurement experiment was performed to analyze the effect of inhomogeneous distribution of tissues. In order to analyze the arm joint's effect on channel transmission, we proposed a piecewise modeling method in combination with connection conditions. It can be seen from the experiment that, in the quasi-static mode, the communication channel error caused by the inhomogeneous distribution of tissues is small enough to be negligible. The error between calculated and experimental results is reduced by 3.93 dB in this experiment relative to models that did not include joint characteristics, and the average error is lowered by 0.73 dB. The variation curve fit to experimental data is also improved in this method. As such, it can be quantitatively determined that a channel model with joint characteristics is superior to models excluding joint characteristics.

Piecewise physical modeling of series resistance and inductance of on-chip interconnects

A physically-based piecewise modeling of the frequency-dependent series resistance and inductance of IC interconnects is presented. The model relies on representing the influence of the frequency-dependent skin and current distribution effects on the characteristics of the interconnects, and detailed explanation of the model parameter extraction is also given. This modeling allows to accurately represent the high-frequency performance of on-chip interconnects by considering the correct and physically expected variation of the series resistance and inductance with frequency. Results in the frequency domain show excellent model-experiment correlations for interconnects fabricated on RF-CMOS technology. Moreover, time domain results were also performed to demonstrate the causality of the proposed model.

Piecewise Modeling of Flow Stress of 7075-T6 Aluminum Alloy in Hot Deformation

Piecewise Modeling of Flow Stress of 7075-T6 Aluminum Alloy in Hot Deformation

A piecewise modeling approach for climate sensitivity studies: Tests with a shallow-water model

In model-based climate sensitivity studies, model errors may grow during continuous long-term integrations in both the “reference” and “perturbed” states and hence the climate sensitivity (defined as the difference between the two states). To reduce the errors, we propose a piecewise modeling approach that splits the continuous long-term simulation into subintervals of sequential short-term simulations, and updates the modeled states through re-initialization at the end of each subinterval. In the re-initialization processes, this approach updates the reference state with analysis data and updates the perturbed states with the sum of analysis data and the difference between the perturbed and the reference states, thereby improving the credibility of the modeled climate sensitivity. We conducted a series of experiments with a shallow-water model to evaluate the advantages of the piecewise approach over the conventional continuous modeling approach. We then investigated the impacts of analysis data error and subinterval length used in the piecewise approach on the simulations of the reference and perturbed states as well as the resulting climate sensitivity. The experiments show that the piecewise approach reduces the errors produced by the conventional continuous modeling approach, more effectively when the analysis data error becomes smaller and the subinterval length is shorter. In addition, we employed a nudging assimilation technique to solve possible spin-up problems caused by re-initializations by using analysis data that contain inconsistent errors between mass and velocity. The nudging technique can effectively diminish the spin-up problem, resulting in a higher modeling skill.

The effects of individually varying times of observations on growth parameter estimations in piecewise growth model

When using latent growth modeling (LGM), researchers often restrict the factor loadings, while the multilevel modeling (MLM) treats time as a metric variable. However, when individually varying times of observations are concerned in the longitudinal studies, the use of specified loadings would lead to inaccurate estimation. Based on piecewise growth modeling (PGM), this simulation study showed that (i) individually varying times of observations with larger boundaries got worse estimates and model fits when LGM was used; (ii) estimating the PGM across all the simulation situations was robust within MLM, whereas LGM got identically equal estimation with MLM only in the case of time boundaries of ±1 month or shorter; (iii) larger change of slope in piecewise modeling indicated better estimation.

A surrogate-model-based identification of fractional viscoelastic constitutive parameters

In order to reduce the computational expense, a Kriging surrogate model is developed as an approximation of a numerical model based on FEM (finite element method) and FDM (finite difference method) to solve direct fractional viscoelastic problems and then is combined with a gridding-partition-based continuous ant colony algorithm to identify constitutive parameters of fractional viscoelastic materials. Three kinds of modeling strategies are presented to generate the Kriging surrogate model, that is, global modeling, piecewise modeling, and reduced modeling. Two numerical examples are given to illustrate the proposed approach in terms of computing accuracy and expense. The utilization of Kriging surrogate model not only can provide a sufficient computing accuracy, but also can significantly reduce the computational cost in solving inverse fractional viscoelastic problems. In addition, regional inhomogeneity and impact of noisy data are taken into account.

Massage on experimental pain in healthy females: A randomized controlled trial

This randomized controlled study evaluated the effect of massage on affect, relaxation, and experimental pain induced by electrical stimulation. Participants were 96 healthy women (M age = 20.13 ± 5.93 years; 84.4% White) randomly assigned to a 15-minute no-treatment control, guided imagery, massage or massage plus guided imagery condition. Multilevel piecewise modeling revealed no group differences in pain intensity, threshold, or tolerance. The two massage conditions generally reported decreased pain unpleasantness, lower unpleasant affect, maintenance of pleasant affect, and increased relaxation compared to the no-treatment condition. The results suggest that massage may alter immediate affective qualities in the context of pain.

Abstract 260: Components of Change Processes of Quality of Life among Stable Coronary Patients with or without Percutaneous Coronary Intervention

Background: Percutaneous coronary intervention (PCI) plus intensive pharmacologic and lifestyle intervention (optimal medical therapy, OMT) to OMT alone was compared in reducing the risk of cardiovascular events in a total of 2,287 patients with stable coronary disease in the COURAGE trial, in which health related quality of life data were measured repeatedly over time for a median of 4.6 years. We examined components of changes in angina-related quality of life over time. Methods: Angina-specific health status was assessed with the Seattle Angina Questionnaire (SAQ). Scores range from 0 to 100; higher scores indicate better health status. Linear piece-wise latent growth curve modeling was conducted to reveal the components of process and risk factors of SAQ score changes over time. Results: For each domain the change process can be divided into two components: a process of improvement from baseline to 6 or 12 months during which the scores significantly increase with a steep upward trajectory till reaching the maximum score value; and followed by a process of stabilization during which the scores kept stable or in a downward slope until the end of the follow-up. Patients in both PCI and OMT alone groups experienced significant increase on SAQ scores over time in each domain. PCI had significant impact in the process of improvement: patients with PCI had about 0.603 (SE=0.193, p-value=0.002) and 0.442(SE=0.22, p-value =0.044) points increase per month in scores in the domains of physical limitation and angina frequency, respectively; the benefit of PCI decreased in the process of stabilization until it disappeared so that the scores in both groups were eventually similar (Table 1). The important risk factors of change in SAQ scores were the use of long-acting nitrates at randomization, the Charlson comorbidity index, and several additional factors that differed by domain. Conclusions: Mechanism and processes of change in angina-related quality of life and health status were significantly related to the impact of adding PCI to OMT. Piece-wise growth curve modeling can be used to capture advantages of PCI over OMT and identify patient characteristics associated with subsequent measures of angina.

Mechanism-Based Pharmacokinetic/Pharmacodynamic Modeling of Rat Prefrontal Cortical Dopamine Response to Dual Acting Norepinephrine Reuptake Inhibitor and 5-HT1A Partial Agonist

Evidence suggests that compounds possessing both norepinephrine reuptake inhibition and 5-HT(1A) partial agonism (NRI/5-HT(1A)) activities may have a greater efficacy in treating neuropsychiatric disorders than compounds possessing either activity alone. The objectives of the present study were first to characterize the pharmacokinetic/pharmacodynamic (PK/PD) relationship of the plasma concentrations of atomoxetine (NRI) and buspirone (5-HT(1A) partial agonist), administered alone and in combination, on the prefrontal cortex dopamine levels in rats, and second to use the model developed to characterize the PK/PD relationship of novel NRI/5-HT(1A) compounds, PF-04269339 and PF-03529936, in a NRI/5-HT(1A) drug discovery program. Maximal dopamine elevation was twofold higher after administration of atomoxetine and buspirone in combination, PF-04269339, or PF-03529936 than after administration of atomoxetine or buspirone alone. A mechanism-based extended indirect response model characterized the time profiles of the prefrontal cortex dopamine response to atomoxetine and buspirone, administered alone or in combination. After fixing three mechanism-specific pharmacodynamic parameters (I (max) and γ2 for NRI and γ1 for 5-HT(1A)) based on the model for atomoxetine and/or buspirone, the model fitted the exposure-response profiles of PF-04269339 and PF-03529936 well. Good in vitro-to-in vivo correlation was demonstrated with the compound-specific pharmacodynamic parameters (IC(50) for NRI and SC(50) and S (max) for 5-HT(1A)) across the compounds. In summary, a piecewise modeling approach was used successfully for the characterization of the PK/PD relationship of novel NRI/5-HT(1A) compounds on prefrontal cortex dopamine levels in rats. The application and value of the mechanism-based modeling in the dual pharmacology drug discovery program are also discussed.

Circuit Design-Oriented Stochastic Piecewise Modeling of the Postbreakdown Gate Current in MOSFETs: Application to Ring Oscillators

A methodology to incorporate the MOSFET gate dielectric breakdown (BD) failure mechanism in the design of complex systems is presented. The model accounts for the statistical nature of the BD phenomenon, is easily extensible to different device geometries and operation conditions (following the established scaling rules for the mechanism), considers the stress history, and can be easily implemented in circuit simulation tools. Device level characterization of the BD mechanism is presented, which is the base for model parameter extraction. The model has been introduced in a circuit simulator to show its suitability for evaluation of the BD effect in circuits and their reliability, taking ring oscillators as example.

The Role of Moist Processes in the Formation and Evolution of Mesoscale Snowbands within the Comma Head of Northeast U.S. Cyclones

Abstract The role of moist processes in regulating mesoscale snowband life cycle within the comma head portion of three northeast U.S. cyclones is investigated using piecewise potential vorticity (PV) inversion, modeling experiments, and potential temperature tendency budgets. Snowband formation in each case occurred along a mesoscale trough that extended poleward of a 700-hPa low. This 700-hPa trough was associated with intense frontogenetical forcing for ascent. A variety of PV evolutions among the cases contributed to midlevel trough formation and associated frontogenesis. However, in each case the induced flow from diabatic PV anomalies accounted for a majority of the midlevel frontogenesis during the band’s life cycle, highlighting the important role that latent heat release plays in band evolution. Simulations with varying degrees of latent heating show that diabatic processes associated with the band itself were critical to the development and maintenance of the band. However, changes in the meso-α-scale flow associated with the development of diabatic PV anomalies east of the band contributed to frontolysis and band dissipation. Conditional stability was reduced near 500 hPa in each case several hours prior to band formation. This stability remained small until band formation, when the stratification generally increased in association with the release of conditional instability. Previous studies have suggested that the dry slot is important for the initial stability reduction at midlevels, but this was not evident for the three banding cases examined. Rather, differential horizontal temperature advection in moist southwest flow ahead of the upper trough was the dominant process that reduced the midlevel conditional stability.

Estradiol Rates of Change in Relation to the Final Menstrual Period in a Population-Based Cohort of Women

The aim was to characterize rates of change in serum estradiol (E2) levels across the menopausal transition and into early postmenopause. We studied the Michigan Bone Health and Metabolism Study cohort of 629 women with median age of 38 yr (interquartile range, 7) at the 1992-1993 baseline with annual assessment of E2 levels over the subsequent 15-yr period. DESIGN/MAIN OUTCOME MEASURES: The purpose was to describe patterns of acceleration/deceleration in (log)E2 rates of change before and after the final menstrual period (FMP) using nonparametric and piecewise regression modeling. Between -10 to -2 yr to the FMP, mean fitted serum E2 population values were relatively stable. The 95% confidence bands around the slight increase in E2 rate of change 5 yr prior to the FMP included the value of no change. The fitted population mean E2 value declined 67% from 64.5 pg/ml (se = 3.6) to 21 pg/ml (se = 1.2) in the 4 yr between -2 < FMP < +2. A second significant mean E2 rate of change was identified from 6-8 yr after FMP. Fitted population mean E2 values declined 18% from 18.1 pg/ml (se = 1.3) at FMP = 6 to 14.8 pg/ml (se = 1.3) at FMP = 8. In nonobese women, the mean E2 percent decline was 42% from FMP = 6 to FMP = 8, whereas in obese women, the mean E2 percent decline over this time was 31%. Population mean serum E2 levels were sustained until approximately 2 yr prior to the FMP. In the ensuing 4-yr period, E2 levels declined 67%. A secondary E2 decline, commencing about 6 yr after the FMP, was observed in nonobese but not obese women.

Follicle Stimulating Hormone and Its Rate of Change in Defining Menopause Transition Stages

The objective of the study was to identify menopause transition stages using acceleration or deceleration patterns of FSH rates of change from the late reproductive years to postmenopause. Participants were the Michigan Bone Health and Metabolism Study cohort of 629 women, aged 24-44 yr (in 1992/3), with 5757 annual FSH data points over a 14-yr period. DESIGN/MAIN OUTCOME MEASURES: The study was designed to relate acceleration/deceleration patterns in FSH rate of change to time to final menstrual period (FMP) and chronological age using nonparametric and piecewise regression modeling. Four major FSH stages, based on rate of FSH change patterns, were identifiable in relation to the FMP. In FSH stage 1, the rate of FSH change increased modestly up to -7 yr prior to the FMP; in FSH stage 2 (-7 to -2 yr prior to FMP), there was a major acceleration in FSH rate of change. FSH stage 3 had an acute increase in FSH rate of change (-2 to +1 yr around the FMP), with average FSH level of 34 mIU/ml. The fourth, or plateau, FSH stage began at 1 yr after FMP when the average FSH level was 54 mIU/ml. During the yr 28-60, there were eight age-specific epochs defined by significant changes of FSH trajectory accelerations or decelerations and rate of change. Four menopause transition stages bounding the FMP and eight epochs in chronological aging from age 28 to 60 yr were defined by changes of FSH trajectory accelerations/decelerations and rates of change. This timing information, combined with knowledge of FSH levels and menstrual cycle characteristics, can help discern the likely status of women with respect to their reproductive viability and menopause transition stage.

外部環境，乗員および空調の影響を考慮した自動車室内温度の区分モデルに基づく動的シミュレーション―実験による検証

The objective of this paper is to discuss the temperature environment model of automobile's passenger compartments for the analysis of comfortableness and the design of air-conditioning control. To consider the comfortableness, it is necessary to model the distribution of the temperature since almost all the people do not feel the uniform temperature environment to be comfortable. In this paper, we construct the dynamical model of the temperature environment by piecewise modeling technique in which the passenger compartment is divided into plural areas to consider the temperature distribution. And then we verify the performance of the model by comparing the simulation results of the model with experimental results.

Piecewise analysis and modeling of circuit pack temperature cycling data

Temperature cycling environmental stress testing (EST) of circuit packs is a standard test procedure for the precipitation of latent defects in order to minimize early product lifecycle customer returns. EST is an expensive, energy-intensive bottleneck in the manufacturing process, one that is based on empiricisms that may be out of date. This presents great opportunity for optimization and test cost reduction. This paper describes the characterization of temperature cycling through analysis and modeling of process data in order to optimize the test parameters — ramp rate, temperature extremes, dwell times, and number of cycles. Failure data from circuit packs tested at a Lucent facility is analyzed using a regression technique and graphical inspection. The dwell and ramp periods of the test are considered in a piecewise manner. A cost model is applied based on distributions fitted to the failure data. The analysis yields a methodology for the dynamic, value-based optimization of temperature cycling EST.