Time-varying process model for intelligent prediction of strain and temperature in micro-turning process

2017 ◽  
Vol 233 (5) ◽  
pp. 1626-1634 ◽  
Author(s):  
Soumen Mandal ◽  
Aniruddha Pal ◽  
Nagahanumaiah
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Process Model ◽  
Maximum Error ◽  
Cutting Edge ◽  
Model Parameters ◽  
Machining Parameters ◽  
Time Varying ◽  
Space Modeling ◽  
Micro Turning

Time-varying process models for micro-machining processes are important as they aid in control of machining parameters. In this research, a state-space-based process model for the temperature and strain generated near the cutting edge of the tool tip is identified using system identification approach. Fiber Bragg grating sensors were placed rigidly near the cutting edge of the tool tip in a micro-turning setup. Subsequently, micro-turning operations were carried out on aluminum and mild steel. The computer numerically controlled program was such that the machining parameters (feed velocity, depth of cut and RPM) change with machining time. The time-varying machining parameters act as inputs to the model, and the dynamic values of strain and temperature serve as model output. A state-space model was generated using the experimental data. Subsequently, a Kalman filter was used to intelligently predict the values of strain and temperature at the cutting edge of tool tip in advance using the model parameters identified by state-space modeling. Experimental results confirm that the time-varying model and the Kalman filter proposed in this research are effective in predicting the strain and temperature in advance with high accuracy. The maximum error in prediction of temperature was 0.4 °C, whereas for strain prediction, the maximum error was 0.3µ∈.

Stable recursive canonical variate state space modeling for time-varying processes

2015 ◽  
Vol 36 ◽  
pp. 113-119 ◽  
Author(s):  
Liangliang Shang ◽  
Jianchang Liu ◽  
Kamuran Turksoy ◽  
Quan Min Shao ◽  
Ali Cinar
Keyword(s):  
State Space ◽  
Canonical Variate ◽  
Time Varying ◽  
Space Modeling ◽  
State Space Modeling

scholarly journals Do Islamic stock indexes outperform conventional stock indexes? A state space modeling approach

2019 ◽  
Vol 28 (3) ◽  
pp. 301-322 ◽  
Author(s):  
Aymen Ben Rejeb ◽  
Mongi Arfaoui
Keyword(s):  
State Space ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Financial Instability ◽  
Informational Efficiency ◽  
Time Varying ◽  
Content Type ◽  
Volatility Spillovers ◽  
Space Modeling ◽  
Stock Indexes

Purpose The purpose of this paper is to investigate whether Islamic stock indexes outperform conventional stock indexes, in terms of informational efficiency and risk, during the recent financial instability period. Design/methodology/approach The paper uses a state space model combined with a standard GARCH(1,1) specification while taking into account structural breakpoints. The authors allow for efficiency and volatility spillovers to be time-varying and consider break dates to locate periods of financial instability. Findings Empirical results show that Islamic stock indexes are more volatile than their conventional counterparts and are not totally immune to the global financial crisis. As regards of the informational efficiency, the results show that the Islamic stock indexes are more efficient than the conventional stock indexes. Practical implications Resulting evidence of this paper has several implications for international investors who wish to invest in Islamic and/or conventional stock markets. Policy makers and even academics and Sharias researchers should as well take preventive measures in order to ensure the stability of Islamic stock markets during turmoil periods. Overall, prudent risk management and precocious financial practices are relevant and crucial for both Islamic and conventional financial markets. Originality/value The originality of this study is performed by the use of time-varying models for volatility spillovers and informational efficiency. It considers structural break dates that think about the dynamic effect of informational flows on stock markets. The study was developed in a global framework using international data. The global analysis allows avoiding country specific effects.

Monitoring pupil development by means of the Kalman filter and smoother based upon sem state space modeling

1998 ◽  
Vol 10 (2) ◽  
pp. 103-119 ◽  
Author(s):  
Johan H.L. Oud ◽  
Robert A.R.G. Jansen ◽  
Jan F.J. van Leeuwe ◽  
Cor A.J. Aarnoutse ◽  
Marinus J.M. Voeten
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Space Modeling ◽  
State Space Modeling

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

2021 ◽  
pp. 1-31
Author(s):  
Yalda Amidi ◽  
Behzad Nazari ◽  
Saeid Sadri ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Point Process ◽  
Goodness Of Fit ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Synaptic Connections ◽  
Synaptic Coupling ◽  
Modeling Framework ◽  
Space Modeling ◽  
Spiking Activity

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

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