Apparent Dependence of Rate- and State-Dependent Friction Parameters on Loading Velocity and Cumulative Displacement Inferred from Large-Scale Biaxial Friction Experiments

2017 ◽  
pp. 23-43
Author(s):  
Yumi Urata ◽  
Futoshi Yamashita ◽  
Eiichi Fukuyama ◽  
Hiroyuki Noda ◽  
Kazuo Mizoguchi
Keyword(s):  
Large Scale ◽  
State Dependent ◽  
Friction Parameters

scholarly journals Apparent Dependence of Rate- and State-Dependent Friction Parameters on Loading Velocity and Cumulative Displacement Inferred from Large-Scale Biaxial Friction Experiments

2016 ◽  
Vol 174 (6) ◽  
pp. 2217-2237 ◽  
Author(s):  
Yumi Urata ◽  
Futoshi Yamashita ◽  
Eiichi Fukuyama ◽  
Hiroyuki Noda ◽  
Kazuo Mizoguchi
Keyword(s):  
Large Scale ◽  
State Dependent ◽  
Friction Parameters

The non-linear effects of the Fed asset purchases

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alessio Anzuini
Keyword(s):  
Financial Markets ◽  
Large Scale ◽  
Balance Sheet ◽  
Monetary Economics ◽  
American Economic Review ◽  
Vector Autoregressive ◽  
Portfolio Balance ◽  
State Dependent ◽  
Linear Effects ◽  
Asset Purchases

Abstract The Federal Reserve responded to the great financial crisis deploying new monetary policy tools, the most notable of which being the expansion of its balance sheet. In a recent paper, Weale, M., and T. Wieladek. 2016. “What Are the Macroeconomic Effects of Asset Purchases?” Journal of Monetary Economics 79 (C): 81–93 show that the asset purchases were effective in stimulating economic activity as well as inflation and asset prices. Here I show that their results are state dependent: large scale asset purchase are effective only when financial markets are impaired. Financial markets are under stress when the effective risk-bearing capacity of the financial sector is drastically reduced, i.e. when the excess bond premium (EBP) of Gilchrist, S., and E. Zakrajšek. 2012. “Credit Spreads and Business Cycle Fluctuations.” The American Economic Review 102 (4): 1692–72 exceed a certain threshold. Using an estimated threshold vector autoregressive model conditional on the EBP regime, I show that an increase in the balance sheet has expansionary effects on GDP and inflation when EBP is high, but not when it is low (as its effects become mostly insignificant). I argue that the high EBP can be interpreted as a proxy of market dis-functioning so that only when this channel of transmission is on, the unconventional policy is particularly effective. This suggests that models of transmission of unconventional policies, based on asset purchases, should focus also on the market functioning channel and not only on the portfolio balance one.

scholarly journals Robust Adaptive Fuzzy Control via State-Dependent Function for Nonlinear Stochastic Large-Scale Systems Subject to Dead Zones

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chang-Qi Zhu ◽  
Lei Liu
Keyword(s):  
Fuzzy Control ◽  
Large Scale ◽  
Dead Zone ◽  
Adaptive Fuzzy Control ◽  
Closed Loop System ◽  
Large Scale Systems ◽  
Adaptive Fuzzy ◽  
State Dependent ◽  
Control Scheme ◽  
Robust Adaptive

This paper concentrates on the adaptive fuzzy control problem for stochastic nonlinear large-scale systems with constraints and unknown dead zones. By introducing the state-dependent function, the constrained closed-loop system is transformed into a brand-new system without constraints, which can realize the same control objective. Then, fuzzy logic systems (FLSs) are used to identify the unknown nonlinear functions, the dead zone inverse technique is utilized to compensate for the dead zone effect, and a robust adaptive fuzzy control scheme is developed under the backstepping frame. Based on the Lyapunov stability theory, it is proved ultimately that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. Finally, an example based on an actual system is given to verify the effectiveness of the proposed control scheme.

scholarly journals Tremor during ice-stream stick slip

2016 ◽  
Vol 10 (1) ◽  
pp. 385-399 ◽  
Author(s):  
B. P. Lipovsky ◽  
E. M. Dunham
Keyword(s):  
Large Scale ◽  
Wait Time ◽  
Stick Slip ◽  
Repeating Earthquakes ◽  
Ice Surface ◽  
Ice Stream ◽  
State Dependent ◽  
State Evolution ◽  
Spectral Peaks ◽  
Balance Of Forces

Abstract. During the 200 km-scale stick slip of the Whillans Ice Plain (WIP), West Antarctica, seismic tremor episodes occur at the ice–bed interface. We interpret these tremor episodes as swarms of small repeating earthquakes. The earthquakes are evenly spaced in time, and this even spacing gives rise to spectral peaks at integer multiples of the recurrence frequency ∼ 10–20 Hz. We conduct numerical simulations of the tremor episodes that include the balance of forces acting on the fault, the evolution of rate- and state-dependent fault friction, and wave propagation from the fault patch to a seismometer located on the ice. The ice slides as an elastic block loaded by the push of the upstream ice, and so the simulated basal fault patch experiences a loading velocity equal to the velocity observed by GPS receivers on the surface of the WIP. By matching synthetic seismograms to observed seismograms, we infer fault patch area ∼ 10 m2, bed shear modulus ∼ 20 MPa, effective pressure ∼ 10 kPa, and frictional state evolution distance ∼ 1 μm. Large-scale slip events often occur twice daily, although skipped events have been increasing in frequency over the last decade. The amplitude of tremor (recorded by seismometers on the ice surface) is greater during the double wait time events that follow skipped events. The physical mechanism responsible for these elevated amplitudes may provide a window into near-future subglacial conditions and the processes that occur during ice-stream stagnation.

Multiplicative Noise and Non-Gaussianity: A Paradigm for Atmospheric Regimes?

2005 ◽  
Vol 62 (5) ◽  
pp. 1391-1409 ◽  
Author(s):  
Philip Sura ◽  
Matthew Newman ◽  
Cécile Penland ◽  
Prashant Sardeshmukh
Keyword(s):  
Large Scale ◽  
Multiplicative Noise ◽  
Probability Distributions ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Stochastic Perturbations ◽  
The Past ◽  
State Dependent ◽  
Non Gaussian ◽  
Noise Statistics

Abstract Atmospheric circulation statistics are not strictly Gaussian. Small bumps and other deviations from Gaussian probability distributions are often interpreted as implying the existence of distinct and persistent nonlinear circulation regimes associated with higher-than-average levels of predictability. In this paper it is shown that such deviations from Gaussianity can, however, also result from linear stochastically perturbed dynamics with multiplicative noise statistics. Such systems can be associated with much lower levels of predictability. Multiplicative noise is often identified with state-dependent variations of stochastic feedbacks from unresolved system components, and may be treated as stochastic perturbations of system parameters. It is shown that including such perturbations in the damping of large-scale linear Rossby waves can lead to deviations from Gaussianity very similar to those observed in the joint probability distribution of the first two principal components (PCs) of weekly averaged 750-hPa streamfunction data for the past 52 winters. A closer examination of the Fokker–Planck probability budget in the plane spanned by these two PCs shows that the observed deviations from Gaussianity can be modeled with multiplicative noise, and are unlikely the results of slow nonlinear interactions of the two PCs. It is concluded that the observed non-Gaussian probability distributions do not necessarily imply the existence of persistent nonlinear circulation regimes, and are consistent with those expected for a linear system perturbed by multiplicative noise.

scholarly journals A state-dependent mean-field formalism to model different activity states in conductance based networks of spiking neurons

2019 ◽  
Author(s):  
Cristiano Capone ◽  
Matteo di Volo ◽  
Alberto Romagnoni ◽  
Maurizio Mattia ◽  
Alain Destexhe
Keyword(s):  
Population Dynamics ◽  
High Performance ◽  
Large Scale ◽  
Mean Field ◽  
Analytic Solutions ◽  
Slow Oscillations ◽  
State Dependent ◽  
The Mean ◽  
Field Formalism ◽  
High Performance Computers

AbstractHigher and higher interest has been shown in the recent years to large scale spiking simulations of cerebral neuronal networks, coming both from the presence of high performance computers and increasing details in the experimental observations. In this context it is important to understand how population dynamics are generated by the designed parameters of the networks, that is the question addressed by mean field theories. Despite analytic solutions for the mean field dynamics has already been proposed generally for current based neurons (CUBA), the same for more realistic neural properties, such as conductance based (COBA) network of adaptive exponential neurons (AdEx), a complete analytic model has not been achieved yet. Here, we propose a novel principled approach to map a COBA on a CUBA. Such approach provides a state-dependent approximation capable to reliably predict the firing rate properties of an AdEx neuron with non-instantaneous COBA integration. We also applied our theory to population dynamics, predicting the dynamical properties of the network in very different regimes, such as asynchronous irregular (AI) and synchronous irregular (SI) (slow oscillations, SO).This results show that a state-dependent approximation can be successfully introduced in order to take into account the subtle effects of COBA integration and to deal with a theory capable to correctly predicts the activity in regimes of alternating states like slow oscillations.

scholarly journals Oscillations, criticality and responsiveness in complex brain networks

2019 ◽  
Author(s):  
MinKyung Kim ◽  
UnCheol Lee
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Conscious State ◽  
Brain Network ◽  
State Dependent ◽  
Model Study ◽  
Brain States ◽  
Specific Stimulation ◽  
The Brain ◽  
Oscillation Properties

AbstractBrain networks during unconscious states resulting from sleep, anesthesia, or traumatic injuries are associated with a limited capacity for complex responses to stimulation. Even during the conscious resting state, responsiveness to stimulus is highly dependent on spontaneous brain activities. Many empirical findings have been suggested that the brain responsiveness is determined mainly by the ongoing brain activity when a stimulus is given. However, there has been no systematic study exploring how such various brain activities with high or low synchronization, amplitude, and phase response to stimuli. In this model study, we simulated large-scale brain network dynamics in three brain states (below, near, and above the critical state) and investigated a relationship between ongoing oscillation properties and a stimulus decomposing the brain activity into fundamental oscillation properties (instantaneous global synchronization, amplitude, and phase). We identified specific stimulation conditions that produce varying levels of brain responsiveness. When a single pulsatile stimulus was applied to globally desynchronized low amplitude of oscillation, the network generated a large response. By contrast, when a stimulus was applied to specific phases of oscillation that were globally synchronized with high amplitude activity, the response was inhibited. This study proposes the oscillatory conditions to induce specific stimulation outcomes in the brain that can be systematically derived from networked oscillator properties, and reveals the presence of state-dependent temporal windows for optimal brain stimulation. The identified relationship will help advance understanding of the small/large responsiveness of the brain in different states of consciousness and suggest state-dependent methods to modulate responsiveness.Author SummaryA responsiveness of the brain network to external stimulus is different across brain states such as wakefulness, sleep, anesthesia, and traumatic injuries. It has been shown that responsiveness of the brain during conscious state also varies due to the diverse transient states of the brain characterized by different global and local oscillation properties. In this computational model study using large-scale brain network, we hypothesized that the brain responsiveness is determined by the interactions of networked oscillators when a stimulus is applied to the brain. We examined relationships between responsiveness of the brain network, global synchronization levels, and instantaneous oscillation properties such as amplitude and phase in different brain states. We found specific stimulation conditions of the brain that produce large or small levels of responsiveness. The identified relationship suggests the existence of temporal windows that periodically inhibit sensory information processing during conscious state and develops state-dependent methods to modulate brain responsiveness considering dynamically changed functional brain network.

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