Transfer Function Estimation Research Articles

Integrated Modeling of Power Network and Connected Flywheel Energy Storage System for Optimal Power and Energy Ratings of Flywheel

Power and energy ratings are the most important parameters of Flywheel Energy Storage System (FESS) which have a crucial influence on its dynamic performance in frequency regulation applications. In order to achieve the optimum dynamic performance of FESS, an integrated model is required which includes key parameters of the power network and FESS. In this paper, an integrated transfer function of FESS connected to a single-bus aggregated power system is presented. The proposed model includes both power network and FESS parameters. Moreover, the model is linearized to facilitate the application of the parameter screening approach which recognizes the most influential parameters through Taguchi method. Afterwards, combined Response Surface Methodology (RSM)-GA optimization algorithm is utilized based on an accurate approximation of the system transfer function. In order to obtain FESS power and energy ratings on the basis of lowest capital price and best dynamic response objective functions, optimization algorithm is used. The simulation and experimental studies confirm the validity of the proposed modelling and optimization approach.

Sequential Estimation of Relative Transfer Function in Application of Acoustic Beamforming

In this paper, a sequential approach is proposed to estimate the relative transfer functions (RTF) used in developing a generalized sidelobe canceller (GSC). The latency in calibrating microphone arrays for GSC, often suffered by conventional approaches involving batch operations, is significantly reduced in the proposed sequential method. This is accomplished by an immediate generation of the RTF from initial input segments and subsequent updates of the RTF as the input stream continues. From the experimental results via the mean square error (MSE) criterion, it has been shown that the proposed method exhibits improved performance over the conventional batch approach as well as over recently introduced least mean squares approaches.

Function Space Optimization: A Symbolic Regression Method for Estimating Parameter Transfer Functions for Hydrological Models.

Estimating parameters for distributed hydrological models is a challenging and long studied task. Parameter transfer functions, which define model parameters as functions of geophysical properties of a catchment, might improve the calibration procedure, increase process realism, and can enable prediction in ungauged areas. We present the function space optimization (FSO), a symbolic regression method for estimating parameter transfer functions for distributed hydrological models. FSO is based on the idea of transferring the search for mathematical expressions into a continuous vector space that can be used for optimization. This is accomplished by using a text generating neural network with a variational autoencoder architecture that can learn to compress the information of mathematical functions. To evaluate the performance of FSO, we conducted a case study using a parsimonious hydrological model and synthetic discharge data. The case study consisted of two FSO applications: single‐criteria FSO, where only discharge was used for optimization, and multicriteria FSO, where additional spatiotemporal observations of model states were used for transfer function estimation. The results show that FSO is able to estimate transfer functions correctly or approximate them sufficiently. We observed a reduced fit of the parameter density functions resulting from the inferred transfer functions for less sensitive model parameters. For those it was sufficient to estimate functions resulting in parameter distributions with approximately the same mean parameter values as the real transfer functions. The results of the multicriteria FSO showed that using multiple spatiotemporal observations for optimization increased the quality of estimation considerably.

Joint MAP channel estimation and data detection for OFDM in presence of phase noise from free running and phase locked loop oscillator

This paper addresses a computationally compact and statistically optimal joint Maximum a Posteriori (MAP) algorithm for channel estimation and data detection in the presence of Phase Noise (PHN) in iterative Orthogonal Frequency Division Multiplexing (OFDM) receivers used for high speed and high spectral efficient wireless communication systems. The MAP cost function for joint estimation and detection is derived and optimized further with the proposed cyclic gradient descent optimization algorithm. The proposed joint estimation and detection algorithm relaxes the restriction of small PHN assumptions and utilizes the prior statistical knowledge of PHN spectral components to produce a statistically optimal solution. The frequency-domain estimation of Channel Transfer Function (CTF) in frequency selective fading makes the method simpler, compared with the estimation of Channel Impulse Response (CIR) in the time domain. Two different time-varying PHN models, produced by Free Running Oscillator (FRO) and Phase-Locked Loop (PLL) oscillator, are presented and compared for performance difference with proposed OFDM receiver. Simulation results for joint MAP channel estimation are compared with Cramer-Rao Lower Bound (CRLB), and the simulation results for joint MAP data detection are compared with “NO PHN” performance to demonstrate that the proposed joint MAP estimation and detection algorithm achieve near-optimum performance even under multipath channel fading.

Double Exponent Fractional-Order Filters: Approximation Methods and Realization

The main goal of this work is to exploit different tools in order to approximate a general double exponent fractional-order transfer function. Through the appropriate selection of the two fractional orders of this function, different types of filters can be derived. The investigated approximation tools are either curve fitting based tools or the Pade approximation tool, and the derived approximated transfer functions in all cases have the form of rational integer-order polynomials, which can be easily realized electronically.

Frequency response function identification of periodically scheduled linear parameter-varying systems

For Linear Time-Invariant (LTI) systems, Frequency Response Functions (FRFs) facilitate dynamics analysis, controller design, and parametric modeling, while many practically relevant systems are in fact more accurately described by Linear Parameter-Varying (LPV) models. The aim of this paper is to develop an FRF modeling framework for periodically scheduled Single-Input Single-Output (SISO) LPV systems, that enables the identification of LPV FRF models from global experiments. This is achieved by developing an appropriate definition of the harmonic FRF for input–output LPV systems and by developing a method to compute a suitable harmonic FRF estimator. The developed approach generalizes the Empirical Transfer Function Estimate (ETFE) to the class of periodically-scheduled LPV systems, and the classical ETFE is recovered for LTI systems as a special case. The developed method is successfully used to estimate a SISO LPV FRF of an experimental motion system, thereby confirming the potential of the developed framework.

Enhanced dynamic equivalent identification method of large-scale power systems using multiple events

The increasing complexity of the interconnected power system makes high-fidelity dynamic simulation models computationally more intensive. To improve computation efficiency, model reduction techniques have been investigated to only preserve the dynamics in a limited area of interest (study area), while deriving equivalent representation for the external area. For this purpose, a measurement-based reduction approach using system identification techniques has been previously proposed. In that case, external areas are represented by dynamic equivalent loads using transfer function estimation. In this paper, in order to enhance the accuracy of the reduced model in preserving dynamics of the study area, multiple “grid” events of different types and at different locations are considered for identifying the parameters of the equivalent loads. Case studies are carried out in the NPCC 140-bus system. Comparisons are made between multiple events training and single event training, highlighting the advantages of the proposed method in providing a better representation of the grid dynamics under different operating conditions.

ANN-based channel estimation algorithm of IM/DD-OFDM/OQAM-PON systems with mobile fronthaul network in 5G

Abstract 5G mobile fronthaul (MFH) network requires data transmission with large bandwidth and ultra-low latency. Orthogonal frequency division multiplexing/offset quadrature amplitude modulation (OFDM/OQAM) passive optical network (PON) system is the 5G MFH solution with low spectrum efficiency and large bandwidth. The OFDM/OQAM-PON transmission performance will be undermined by intrinsic imaginary interference (IMI) induced by chromatic dispersion (CD) and polarization mode dispersion (PMD). In this paper, we proposed an artificial neural network (ANN) based channel estimation (CE) algorithm, which can effectively reduce IMI by estimation of channel transfer function (TF). Simulation results show that the proposed algorithm can optimize the system performance, compared with the conventional LS method, the proposed algorithm can improve bit error rate optimization capability by an order of magnitude.

Rational Transfer Function Model for a Double-Pipe Parallel-Flow Heat Exchanger

Transfer functions of typical heat exchangers, resulting from their partial differential equations, usually contain irrational functions which quite accurately describe the spatio-temporal nature of the processes occurring therein. However, such an accurate but complex mathematical representation is often not convenient from the practical point of view, and some approximation of the original model would be more useful. This paper discusses approximate rational transfer functions for a typical thick-walled double-pipe heat exchanger working in the parallel-flow configuration. Using the method of lines with the backward difference scheme, the original symmetric hyperbolic partial differential equations describing the heat transfer phenomena are transformed into a set of ordinary differential equations and expressed in the form of N subsystems representing spatial sections of the exchanger. Each section is described by a rational transfer function matrix and their cascade interconnection results in the overall approximation model expressed by a matrix of rational transfer functions of high order. Based on the rational transfer function representation, the frequency and steady-state responses of the approximate model are evaluated and compared with those resulting from its original irrational transfer function model. The presented results show better approximation quality for the “crossover” input–output channels where the in-domain heat conduction effects prevail as compared to the “straightforward” channels, where the transport delay associated with the heat convection dominates.

Low-Cost Experimental Methodology for the Dynamic Model Approximation of Multirotor Actuators

A methodology for the experimental modelling of the electric actuators of a multirotor is presented in this work. These actuators are usually brushless DC motors which are driven by electronic speed controllers in an open loop. The duty cycle of a PWM signal, generated by the electronic control unit, is the input of the electronic controller. However, during the control design procedure for the multirotor, it is important to account with a model of the actuators as its dynamical features define the closed-loop performance of the overall aircraft. Hence, a procedure, based on low-cost electronic components, to obtain approximated transfer functions of the actuators of a multirotor is presented. Moreover, as the proposed signal processing algorithms are simple, the computational capabilities of the required embedded system are also low. Given that different control schemes require different information from the actuator, two models were obtained: a duty cycle vs. angular velocity transfer function and a duty cycle vs. consumed current transfer function. The effectivity of the proposal is validated with experimental results on common electric actuators of a multirotor.

A transfer function modelling of Nigerian current account (Net) and exchange rate

This study proposes a transfer function noise model for the Nigerian current account (net) using the US dollar exchange rate as the input variable. The sixty annually collected time series data used covering the period 1960-2019 were obtained from the National Bureau of Statistics. Both series were non-stationary and were transformed by differencing. The transformed series were confirmed stationary using an appropriate test, the augmented Dickey-Fuller test, ADF. The cross-correlation function method was used for the estimation of the transfer model while the Box-Jenkins autoregressive integrated moving average method was used to separately model the autocorrelated noise term. The estimated transfer function with a noise model was subjected to a diagnostic check. The calculated Q-statistic offered evidence for the adequacy of the estimated model. For parsimony, estimates for the model parameters were recursively obtained after factorization.

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.

An Integer-Order Transfer Function Estimation Algorithm for Fractional-Order PID Controllers

Fractional-order systems and controllers have been extensively used in many control applications to achieve robust modeling and controlling performance. To implement these systems, curve fitting based integer-order transfer function estimation techniques namely Oustaloup and Matsuda are most widely used. However, these methods are failed to achieve the best approximation due to the limitation of the desired frequency range. Thus, this article presents a simple curve fitting based integer-order transfer function estimation method for fractional-order differentiator/integrator using frequency response. The advantage of this technique is that it is simple and can fit the entire desired frequency range. Using the approach, an approximation table for fractional-order differentiator has also been obtained which can be used directly to obtain the approximation of fractional-order systems. A simulation study on fractional systems shows that the proposed approach produced better parameter approximation for the desired frequency as compared to Oustaloup, refined Oustaloup and Matsuda techniques.

MÔ HÌNH HÓA VÀ ƯỚC LƯỢNG PHẦN TỬ LIÊN KẾT ĐÀN HỒI TRONG CÁC HỆ TRUYỀN ĐỘNG ĐIỆN TỰ ĐỘNG NHIỀU ĐỘNG CƠ BẰNG PHƯƠNG PHÁP SỐ

The paper proposes a new approach for solving the problem of determining the optimal estimation model of the elastic linkage element in multi-motor automatic driving systems using the real interpolation method (RIM). Algorithms and programs based on the RIM have been built for a two-motor electromechanical driving model to approximate the original transfer function that describes the elastic linkage element containing inertialand transcendent components by the transfer function in a rational fraction form. Besides,providing a specific function form, the program also calculates estimation errors for different approximate transfer functions, thereby allowing to determine the structure and parameters of the optimal estimation model.

Data-driven Optical Fiber Channel Modeling: A Deep Learning Approach

A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on-off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data pattern, pulse shaping, and sample rate. The satisfactory fitting results and acceptable mean square errors indicate that the approximate transfer function of the fiber channel is learned by the BiLSTM. Moreover, compared with repetitive iteration SSF, the computing time is significantly reduced by the BiLSTM owing to its independence on fiber length and insensitivity to data size and launch power. Our aim is to demonstrate the BiLSTM is comparable with the conventional model-driven SSF-based method for direct-detection optical fiber system. We think the proposed method could be a supplementary technique that can be used for the existing simulation system and could also be a potential option for future simulation methods.

Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels

In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), where DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e., channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

Fractional integrator circuit unit using Charef approximation method

This paper deals with the reduction of the approximated transfer function based on the Charef approximation method. The reduced transfer function is utilized for the realization of fractional integrator circuit unit. This model takes few components than the existent one and gives more accuracy according to the frequency of interest. In our investigation, we show the accuracy of the approximation in 2 dB and we specify our study in two domains in which chaotic systems use to operate. We identify the corresponding values of resistors and capacitors of $$\alpha $$ for the frequency of interest. Moreover, we realized electronic circuit of two fractional order chaotic system with the proposed chain circuit unit. Comparing numerical simulations to electronic circuits realization, these results are compatibles.

Site Effects of Onna during the 2009 L’Aquila (Central Italy) Seismic Sequence: Constraints on Bedrock Depth and 1D Local Velocity Structure from Aftershock Seismograms

ABSTRACTAfter the 2009 L’Aquila Mw 6.1 earthquake, particular attention was paid to the large difference of Mercalli–Cancani–Sieberg (MCS) macroseismic intensity between the nearby villages of Onna (9.5 MCS) and Monticchio (6 MCS). Several authors estimated that in Onna, settled in the Aterno river valley, ground motions were amplified at 2–3 Hz by up to a factor of 5 with respect to Monticchio, settled on more competent rocks. Although there was a general agreement that the spectral peak was caused by the resonance of the uppermost 40 m layer, a satisfactory fit of the amplitudes was not provided. Here, we apply spectral ratio techniques to 1437 aftershock seismograms (magnitude between 1.8 and 3.9) to compare ground motions within Onna and between Onna and Monticchio. Spectral amplitudes at stations located outside and inside the “red zone” of Onna show that the seismic response was uniform, confirming that vulnerability was crucial for the heavier damage of the ancient part of the village. We have also estimated the empirical transfer function of Onna through the spectral ratios between Onna and Monticchio. Although in a 1D simplification, a model with a further velocity contrast of ∼2 at 200 m of depth produces a more accurate fit of observations. Using the new velocity profile, we modeled the mainshock ground motion at Onna in an equivalent-linear approach. Accelerations are amplified by a factor of 2 and spectral ordinates increase from 0.7g at 0.2 s to 1g at 0.5 s, a shaking level that can be destructive for nonductile ancient buildings. This study shows that accurate estimates of empirical transfer functions, even in a simplified 1D approach, provide useful constraints to the deeper velocity structure where measurements are shallow or lacking.

Estimation of contact heat transfer between curvilinear contacts using inverse method and group method of data handling (GMDH)-type neural networks

Precise knowledge of thermal contact conductance (TCC) across fixed solid contacts of flat-flat and curvilinear surfaces is becoming a crucial topic in advanced industrial applications such as thermal management of electronic packaging and nuclear energy production. In this study, at first, TCC across fixed solid contacts of flat-flat and cylinder-cylinder contacts has been calculated by inverse solution method. Furthermore, the accuracy of inverse method for estimation of TCC between flat-flat and cylinder-cylinder contacts has been evaluated based on the error analysis. Then, the GMDH (group method of data handling) algorithm has been used for heat transfer function estimation between contact surfaces based on input–output data which has been achieved from the experimental investigation and the inverse method using the Conjugate Gradient Method (CGM) with Adjoint problem. Different models of GMDH algorithm are applied and the optimal model is selected based on the common error criteria of RMSE (root mean square error). According to the results, the inverse method is accurate enough to predict TCC between flat-flat and cylinder-cylinder contacts with the root mean square error of 0.167 and 0.205, respectively. Also, it has shown that among the different models of GMDH algorithm, Genetic algorithm obtained by SVD method, make the best algorithm for TTC identification in fixed flat-flat and cylinder-cylinder contacting surfaces. Meanwhile, the attained Root Mean Square Error (RMSE) of the Genetic algorithm obtained by SVD method compared with the experimental results for flat-flat and cylinder-cylinder are 0.1459 and 0.1758, respectively.

Defect classification of refrigerant compressor using variance estimation of the transfer function between pressure pulsation and shell acceleration

This paper deals with a defect classification technique that considers the structural characteristics of a refrigerant compressor. First, the pressure pulsation of the refrigerant flowing in the suction pipe of a normal compressor was measured at the same time as the acceleration of the shell surface, and then the transfer function between the two signals was estimated. Next, the frequency-weighted acceleration signals of the defect classification target compressors were generated using the estimated transfer function. The estimation of the variance of the transfer function is presented to formulate the frequency-weighted acceleration signals. The estimated frequency-weighted accelerations were applied to defect classification using frequency-domain features. Experiments were performed using commercial compressors to verify the technique. The results confirmed that it is possible to perform an effective defect classification of the refrigerant compressor by the shell surface acceleration of the compressor. The proposed method could make it possible to improve the total inspection performance for compressors in a mass-production line.