Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise

Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares (RGELS) and recursive Maximum Likelihood (RML) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed.     

Prism Signal Processing of Coriolis meter data for gasoline fuel injection monitoring

Prism Signal Processing is a new recursive FIR technique offering rapid filter design and calculation. It has previously been applied to Coriolis mass flow metering to generate fast (48 kHz) flow measurement updates, facilitating for the first time the direct mass flow measurement of individual fuel pulses injected into a laboratory diesel fuel injection test bench. In this paper we describe an augmented sensor signal filtering scheme which enables rapid tracking of the desired mode of flow tube vibration while notching out undesired modes. The new scheme is applied to a gasoline injection test bench where the vibrational interference is greater than for the previously described diesel system due to increased hydraulic shock. The paper presents experimental findings which illustrate the further challenges to be overcome in order to achieve the goal of traceable direct mass flow measurement of individual fuel injection pulses. For example, when a fuel pulse is shorter than the resonant period of the flow tube, the observed phase difference appears to show dependence on the instantaneous phase of the flow tube vibration.     

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition☆

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such...     

Recursive parameter estimation of exhaust gas oxygen sensors with input-dependent time delay and linear parameters

Exhaust gas oxygen sensors are widely used for emission control in internal combustion engine systems. Due to their working principle and their positioning, these sensors are subject to input-dependent time delays and input-dependent linear parameters. Consequently, the corresponding time delays and linear parameters can vary fast, i.e. at the same rate as the respective input signals. This paper presents an extension of an existing gradient-based least-squares algorithm and its application to recursively estimate the input-dependent time delays and linear parameters of wide-range oxygen sensors in diesel engines. The extended algorithm is applied in a detailed simulation and experimental study involving real wide-range oxygen sensors that are affected by drift, aging, clogging and manipulation. The input-dependent time delay and linear parameter estimates obtained with the proposed recursive algorithm accurately reproduce the estimates obtained with a numerical offline optimization procedure.     

Factors affecting forestland owners' allocation of non-forested land to pine plantation for bioenergy in Virginia

Studies have shown that woody bioenergy can have potential economic, social, and environmental benefits. One of the ways to meet the growing biomass demand for woody bioenergy is by allocating currently non-forested land for growing feedstocks such as pine. Towards this end, we conducted a survey on 900 randomly selected private forestland owners in Virginia and asked what proportion of their non-forested land they would allocate for loblolly pine at given bid prices. We then used recursive partitioning based Tobit regression to analyze data. Our results suggest that the experience of having supplied wood for chip-n-saw mills in the past five years, large land holding, prior experience with state/federal financial/technical support programs, among other factors, lead to smaller proportion of non-forested land being allocated for pine. However, a higher price offer, stronger preference for producing non-timber forest products such as evergreen boughs and grapevine, and lesser dependence on working the land for annual income lead to larger proportion of non-forested land being allocate for pine. Our results could assist policy makers in developing and improving land use and energy policies, certification programs, and extension and outreach services. Our contribution also includes the use of threshold analyses to delineate tipping points in variables associated with different response rates and showing the different effect of variables in terms of how they affect the supply of biomass and the supply of land for bioenergy purposes.     

Two-dimensional discrete cosine transform on sliding windows

The discrete cosine transform (DCT) has been successfully used for a wide range of applications in digital signal processing. While there are efficient algorithms for implementing the DCT, its use becomes difficult in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated. In this paper, a new two-dimensional sliding DCT (2-D SDCT) algorithm is proposed for fast implementation of the DCT on 2-D sliding windows. In the proposed algorithm, the DCT coefficients of the shifted window are computed by exploiting the recursive relationship between 2-D DCT outputs of three successive windows. The theoretical analysis shows that the computational requirement of the proposed 2-D SDCT algorithm is the lowest among existing 2-D DCT algorithms. Moreover, the proposed algorithm enables independent updating of each DCT coefficient.     

Reliability of an Independent Component,s-Spare System with Exponential Life Times and General Repair Times

In this paper, the reliability of a system with N independent components supplied with a pool of s spares is investigated. The components have an exponential life time distribution and the failed components are repaired, one at a time, with a general repair time distribution. Numerical results are presented for the mean time to system failure (system is said to fail when a component fails with no spare available) and the reliability of the system in some special cases of interest.     

Approximation analysis of multi-class closed queueing maintenance networks with a parts inventory system and two-phase Coxian time distributions

We consider a maintenance network where a set of bases is supported by a replacement parts inventory system and a centrally located repair depot. The ordering policy for the parts is the (S, Q) inventory policy. We extended the previous results to the network, where processing times at each node follow a two-phase Coxian distribution. The proposed network was modeled as a multi-class closed queueing network with a synchronization station. To make the analysis of the network computationally tractable, we developed a two-phase approximation method. In the first phase of the method, the proposed network was analyzed with the previous algorithm based on a product-form approximation. In the second phase, a sub-network was again analyzed with the procedure of a product-form approximation method such that the state space of the sub-network was reduced. In the analysis of a sub-network, a recursive method was also used to solve balance equations by exploiting the special structure of the Markov chain. The new algorithm provided a good estimation of the performance measures of interest. In addition to being accurate, the new algorithm is simple and converges rapidly.     

Recursive implementation of MUSIC algorithm to minimize power system disturbances

This paper exhibits new recursive implementation of the root-MUSIC algorithm in power system applications in order to extract the instantaneous power disturbances, which emanate from the arc furnace. The MUSIC algorithm is selected because it has an outstanding capability to estimate and track the frequencies of all types of distortion such as harmonics and interharmonics. The utilized algorithm has been newly developed so as to recursively track the instantaneous disturbances in the arc furnace current and consequently mitigate them. The suggested development of the recursive root-MUSIC is compared with the ESPRIT in order to prove that the adopted technique outperforms the high-resolution common techniques in a noisy or non-noisy environment. The instantaneous disturbance is formed by an innovative formulation of the recursive notch filter. Moreover, the experimental results are given to prove the practicality of the proposed concepts and techniques.     

Least squares based and gradient based iterative identification for Wiener nonlinear systems

This paper derives a least squares-based and a gradient-based iterative identification algorithms for Wiener nonlinear systems. These methods separate one bilinear cost function into two linear cost functions, estimating directly the parameters of Wiener systems without re-parameterization to generate redundant estimates. The simulation results confirm that the proposed two algorithms are valid and the least squares-based iterative algorithm has faster convergence rates than the gradient-based iterative algorithm.