Time-domain Input Research Articles

On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model

The accuracy of identifying the parameters of models describing lithium ion batteries (LIBs) in typical battery management system (BMS) applications is critical to the estimation of key states such as the state of charge (SoC) and state of health (SoH). In applications such as electric vehicles (EVs) where LIBs are subjected to highly demanding cycles of operation and varying environmental conditions leading to non-trivial interactions of ageing stress factors, this identification is more challenging. This paper proposes an algorithm that directly estimates the parameters of a nonlinear battery model from measured input and output data in the continuous time-domain. The simplified refined instrumental variable method is extended to estimate the parameters of a Wiener model where there is no requirement for the nonlinear function to be invertible. To account for nonlinear battery dynamics, in this paper, the typical linear equivalent circuit model (ECM) is enhanced by a block-oriented Wiener configuration where the nonlinear memoryless block following the typical ECM is defined to be a sigmoid static nonlinearity. The nonlinear Weiner model is reformulated in the form of a multi-input, single-output linear model. This linear form allows the parameters of the nonlinear model to be estimated using any linear estimator such as the well-established least squares (LS) algorithm. In this paper, the recursive least square (RLS) method is adopted for online parameter estimation. The approach was validated on experimental data measured from an 18650-type Graphite/Lithium-Nickel-Cobalt-Aluminium-Oxide (C6/LiNiCoAlO2) lithium-ion cell. A comparison between the results obtained by the proposed method and by nonparametric frequency-based approaches for obtaining the model parameters is presented. It is shown that although both approaches give similar estimates, the advantages of the proposed method are (i) the simplicity by which the algorithm can be employed on-line for updating nonlinear equivalent circuit model (NL-ECM) parameters and (ii) the improved convergence efficiency of the on-line estimation.

Integral force feedback control with input shaping: Application to piezo-based scanning systems in ECDLs.

In this paper, a hybrid control system is developed by integrating the closed-loop force feedback and input shaping method to overcome the problem of the hysteresis and dynamic behavior in piezo-based scanning systems and increase the scanning speed of tunable external cavity diode lasers. The flexible hinge and piezoelectric actuators are analyzed, and a dynamic model of the scanning systems is established. A force sensor and an integral controller are utilized in integral force feedback (IFF) to directly augment the damping of the piezoelectric scanning systems. Hysteresis has been effectively eliminated, but the mechanical resonance is still evident. Noticeable residual vibration occurred after the inflection points and then gradually disappeared. For the further control of mechanical resonance, based on the theory of minimum-acceleration trajectory planning, the time-domain input shaping method was developed. The turning sections of a scanning trajectory are replaced by smooth curves, while the linear sections are retained. The IFF method is combined with the input shaping method to control the non-linearity and mechanical resonance in high-speed piezo-based scanning systems. Experiments are conducted, and the results demonstrate the effectiveness of the proposed control approach.

Implementation of Discrete Fourier Transform And Orthogonal Discrete Wavelet Transform In Python

This paper presents implementation of Discrete Fourier Transform and Orthogonal Discrete Wavelet Transform in Python computer programming language. The Fourier Transform is a fundamental signal processing tool whereas the Wavelet Transform is a powerful and advanced signal processing tool. Both have applications in numerous scientific and engineering disciplines. Our implementation aims to develop a deeper understanding of these transformations by presenting detailed coding steps to generate the frequency-domain and wavelet-domain outputs for selected example time-domain input signals. The results generated from developed program code are compared using built-in functions with similar matches have shown the successful implementation.

Ratio of the Worst Case Noise and the Impedance of Power Distribution Network

The classical method of designing power distribution networks (PDNs) is to control the target impedance across a broad frequency range. This methodology is based on the assumption that there is an upper bound for the ratio of the time-domain maximum output voltage noise to the product of target impedance and time-domain maximum input current. In this paper, we analyze the mathematical relation between the time-domain voltage response and the frequency-domain impedance of PDN. We present the closed-form expressions of the maximum ratio for the series RL/RC circuit and LC tank cases in PDN structures. We observe that the maximum ratio for LC tank case is 1.5. Our results show that the worst case noise is not only determined by target impedance, but also depended on the shape of the output impedance profile. A complete PDN path is demonstrated with the worst case ratio of greater than one. We further propose a method to predict the worst case noise of the complete PDN path. The average prediction error of the proposed method is 7% under different PDN cases.

Classification of Faults on Transmission lines using EMTP and Wavelet Multiresolution Analysis

This paper presents a technique for classification of faults on single circuit transmission line using Wavelet Multiresolution Analysis.EMTP (Microtran) is used to perform simulations which generate time domain input signal on a 400KV, 300km transmission line fed from one end. Daubechies eight (D-8)wavelet transforms of the three phase currents on a transmission line fed from one end are used for fault analysis in this work. The summation of the 1 level output of MRA detail signals of current in each phase extracted from the original signals generated are employed in fault classification algorithm. Simulation results show that the proposed method is effective and simple and give accurate results irrespective of fault location, fault inception angle and fault impedance.

A Wavelet Approach for Identification of Linear Time Invariant System

Wavelet transformation has been applied to various problems of system identification. In this paper, a wavelet based approach for the identification of time-invariant system is proposed. In this approach, mother wavelet is used for excitation to find the impulse response, which can be estimated at half the available number of points of the sampled output sequence. This method has been compared with some other standard techniques such as frequency chirp, coherence function and inverse filtering. In chirp method, wideband excitation such as frequency chirp is used. Frequency response is obtained as the DFT of the output of the system for time-domain input. Inverse method uses SVD function to find pseudoinverse. Coherence function has been used to identify the system using MATLAB function tfestimate. The performances of the methods are demonstrated by means of experimental investigation.

4G Security Using Physical Layer RF-DNA with DE-Optimized LFS Classification

Wireless communication networks remain underattack with ill-intentioned “hackers” routinely gaining unauthorized access through Wireless Access Points(WAPs)–one of the most vulnerable points in an informationtechnology system. The goal here is to demonstrate thefeasibility of using Radio Frequency (RF) air monitoring to augment conventional bit-level security at WAPs. The specific networks of interest are those based on Orthogonal Frequency Division Multiplexing (OFDM), to include 802.11a/g WiFi and 4G 802.16 WiMAX. Proof-of-concept results are presented to demonstrate the effectiveness of a “Learningfrom Signals” (LFS) classifier with Gaussian kernel bandwidth parameters optimally determined through DifferentialEvolution (DE). The resultant DE-optimized LFS classifier is implemented within an RF “Distinct Native Attribute” (RFDNA) fingerprinting process using both Time Domain (TD) and Spectral Domain (SD) input features. The RF-DNA isused for intra-manufacturer (like-model devices from a given manufacturer) discrimination of IEEE compliant 802.11a WiFi devices and 802.16e WiMAX devices. A comparative performance assessment is provided using results from the proposed DE-optimized LFS classifier and a Bayesian-based Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classifier as used in previous demonstrations. The assessment is performed using identical TD and SD fingerprint features for both classifiers. Finally, the impact of Gaussian, triangular, and uniform kernel functions on classifier performance is demonstrated. Preliminary resultsof the DE-optimized classifier are very promising, with correct classification improvement of 15% to 40% realized over the range of signal to noise ratios considered.

A VLSI Implementation of Filterbank for MPEG2/4 AAC Encoder

In this paper, we proposed a VLSI architecture of the modified discrete cosine transform (MDCT) for MPEG 2/4 AAC encoders. The MDCT transforms the time domain input signals to the frequency domain spectrums. It is considered one of the most computational intensive part in implementing the AAC encoder. The AAC encoder allows four types of audio blocks while encoding the audio files. With our algorithm, the proposed design can share the coefficients needed in the MDCT. Moreover, we used a 2-dimensional folding technique to reduce the hardware cost while maintaining the audio quality. The proposed design is realized in TSMC 0.18-um 1P6M technology and is operated at 50 MHz. With these techniques on special hardware design, the result shows some advantages on low complexity.

Forecasting stock indices with wavelet domain kernel partial least square regressions

Abstract Financial time series are nonlinear and non-stationary. Most financial phenomena cannot be clearly characterized in time domain. Therefore, traditional time domain models are not very effective in financial forecasting. To address the problem, this study combines wavelet analysis with kernel partial least square (PLS) regressions for stock index forecasting. Wavelet transformation maps time domain inputs to time-frequency (or wavelet) domain, where financial characteristics can be clearly identified. Because of the high dimensionality and heavy multi-collinearity of the input data, a wavelet domain kernel PLS regressor is employed to create the most efficient subspace that maintains maximum covariance between inputs and outputs, and to perform final forecasting. Empirical results demonstrate that the proposed model outperforms traditional neural networks, support vector machines, GARCH models, and has significantly reduced the forecasting errors.

Identification of the structure parameters using short-time non-stationary stochastic excitation

In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input–output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

A dynamic programming algorithm for input estimation on linear time-variant systems

A time domain input estimation algorithm for linear systems with general time-varying parameters is developed. The algorithm is an extension of an existing approach for time-invariant state space models and several new features, such as higher order input approximations and an extended time-variant output relation including direct input influence, are introduced. Numerical examples are given to illustrate the new features and show that the algorithm is valid in a general time-variant setting. In particular, excellent results are obtained for an ill-posed moving force identification problem with noise-contaminated data, treated with Tikhonov regularization.

Application of wavelet multiresolution analysis for identification and classification of faults on transmission lines

This paper presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line fed from both ends are used. The peak absolute value, the mean of the peak absolute values and summation of the 3rd level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are also examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 230 kV, 200 km long line fed from both ends and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also.

A Wavelet Multiresolution-Based Analysis for Location of the Point of Strike of a Lightning Overvoltage on a Transmission Line

Lightning flashes on extremely high voltage lines are quite common and can lead to insulation breakdown or other line hardware failure causing disruption in supply. An accurate location of the site of failure will result in faster maintenance and restoration of supply. In this paper, a novel technique based on the wavelet multiresolution analysis (MRA) for locating the point of strike of a lightning overvoltage on a transmission line is presented. Lightning strikes at different points on a 400-kV transmission line of different line lengths are simulated by shifting the point of strike of the lightning impulse every 5 km. The Electromagnetic Transient Program (EMTP) [Microtran] is employed to generate the time-domain input signal. Daubechies eight (D8) Wavelet transform is used to analyze lightning overvoltages. The tenth level output of MRA detail signals extracted from the original signals is used as the criterion for the analysis. Extensive simulation work has been done and results indicate that the proposed scheme is very effective in locating the point of strike with reasonable accuracy.

Identification and Classification of Faults on Transmission Lines Using Wavelet Multiresolution Analysis

This article presents a new method for identification and classification of faults based on wavelet multiresolution analysis (MRA). Daubechies eight (D-8) wavelet transforms of the three phase currents on a transmission line are used. The peak absolute value, the mean of the peak absolute values, and summation of the third level output of MRA detail signals of current in each phase extracted from the original signals are used as the criterion for the analysis. The effects of fault distance, fault inception angle and fault impedance are examined. Extensive simulations are carried out to generate time domain input signal using EMTP (Microtran) on a 400 kV, 300 km long line and simulation results show that the proposed method is a simple, effective and robust method suitable for high impedance faults also.

On Frequency-Domain and Time-Domain Input Shaping for Multi-Mode Flexible Structures

The technique of input shaping has been successfully applied to the problem of maneuvering flexible structures without excessive residual vibration. Because a shaper is designed such that vibration is eliminated at the end of the shaped input, a short shaper length means that vibration is eliminated sooner. As different shaper design methods yield different shapers, it is advantageous to know how the shaper lengths of these different methods compare. In this paper we draw comparisons between time-domain input shaping methods and frequency-domain input shaping methods after outlining conditions when non-negative amplitude shapers exist when using frequency-domain methods.

A wavelet multiresolution analysis for location of faults on transmission lines

Faults on EHV lines are quite common. They cause disruption in power supply. Accurate location of faults will result in faster maintenance and restoration of supply. This paper presents a new method for the location of faults based on wavelet multiresolution analysis (MRA). EMTP (Microtran) is employed to generate the time domain input signal. Daubechies eight (D-8) wavelet transforms of the three phase currents on transmission lines from both the ends are used. The effects of fault inception angle and fault impedance are examined. Extensive simulation work has been carried out and results indicate that the proposed method is very effective in locating the fault with a high accuracy.

On closed-loop system identification using polyspectral analysis given noisy input–output time-domain data

The problem of closed-loop system identification given noisy input–output measurements is considered. It is assumed that the closed-loop system operates under an external non-Gaussian input which is not measured. If the external input has non-vanishing integrated bispectrum (IB) and data IB is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary with vanishing IB. If the external input has non-vanishing integrated trispectrum (IT) and data IT is used for identification, then the various disturbances/noise processes affecting the system are assumed to be zero-mean stationary Gaussian. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed-loop system must be stable but it is allowed to be unstable in open loop. Parametric modeling of the various noise sequences affecting the system is not needed. First the open-loop transfer function is estimated using the integrated polyspectrum and cross-polyspectrum of the time-domain input–output measurements. Then two existing techniques for parametric system identification given consistent estimates of the underlying transfer function, are exploited. The parameter estimators are strongly consistent. Asymptotic performance analysis is also carried out. A computer simulation example using an unstable open-loop system is presented to illustrate the proposed approach.

Potential of the back propagation neural network in the assessment of gait patterns in ankle arthrodesis

Objective. The purpose of this study was to recognize gait pattern in ankle arthrodesis by using a neural network trained with time domain input and compare the performance of the neural network with the statistical method. Design. Three-layered feed-forward back propagation neural network and a statistical method were used to classify gait patterns of patients with ankle arthrodesis and normal subjects. Background. Although backpropagation neural networks are very efficient in many pattern recognition tasks, they have not been used for gait pattern recognition of ankle arthrodesis. Methods. A total of eighteen parameters measured by force platforms, including nine force parameters and their chronologic incidence of occurrence, were used to classify gait patterns. Results. The results showed that the neural network model was able to classify subjects with recognition rates up to 95.8%. In contrast, the statistical method was only able to classify the subjects with recognition rates of 91.5%. Conclusions. The backpropagation neural network method has better accuracy than the statistical method in discriminating subjects and the time domain features carry important prognostic information. Relevance It is important to be able to quantify the changes in gait pattern after arthrodesis to understand the clinical implications of arthrodesis.

Direct synthesis of microwave filters using inverse scattering transmission-line matrix method

This paper proposes a new design procedure for planar microwave filters based on the inversion of the one-dimensional transmission-line matrix (TLM) method. The essence of the technique is the solution of the inverse scattering problem using a TLM-based algorithm instead of using equivalent circuits. The procedure consists of determining the geometry of the obstacle that generates the desired scattered field. In the case of filters, this field is the time-domain input reflection coefficient, and the geometry is the impedance profile of the filter. The procedure was validated with the design of low-pass and bandstop filters. It can be used to create filters with arbitrary characteristics.

Selection of an appropriate model for the interpretation of time-domain airborne electromagnetic data for geological mapping

A detailed analysis of the time-domain INPUT airborne electromagnetic response to a horizontal layer of variable conductivity and thickness reveals that there are combinations of conductivity and thickness of the layer for which the response behaves like that of a thin sheet (thickness ≪ diffusion depth), those for which it behaves like that of a finite layer (thickness between those of thin sheet and half-space) and those for which it behaves like that of a half-space (thickness ≫ diffusion depth). plots of thickness versus conductivity at which the response changes from one category of behaviour to another produces three distinct zones we have referred to as ‘thin sheet response zone’, ‘finite layer response zone’ and ‘half-space response zone’, respectively. the boundaries between these three zones move to higher conductances with increasing sample times.A damped least-squares inversion of the synthetic time-domain airborne electromagnetic response involving all the six channels of the INPUT system and based on singular value decomposition produces a distinct ‘boundary’ separating pairs of layer conductivity and thickness which can be uniquely resolved from those which cannot. The results further show that conductivity and thickness pairs within the thin-sheet response zone, as expected, cannot be uniquely resolved but those within the finite-layer response zone can be resolved.Using carefully interpreted conductivities and thicknesses of the conductive weathered layer from reconnaissance ground resistivity sounding data from an area flown earlier with an INPUT system, I demonstrate how to apply the general ‘response diagram’ arising from the above results to select between a thin sheet, finite layer and half-space model for the interpretation of time-domain airborne electromagnetic data for geological mapping.