Estimation of the model parameters in equivalent circuit models of potential transformers

ABSTRACTAs elastic electrical connectors, fuzz buttons provide a vertical and solderless electrical interconnection in microwave modules to enhance the integration. However, prolonged use in harsh environments poses a risk of potential failure in electronic components, potentially compromising communication system reliability. This work studies the impact of fuzz button degradation in harsh environments on analog modulation (AM) and pseudo random binary sequence (PRBS) signal transmission using theoretical analysis and experimental testing. Accelerated tests are designed to obtain the fuzz button samples with different degradation levels. The surface morphology observation and elemental analysis are conducted to analyse the degradation mechanism. In addition, a transmission channel with fuzz button interconnections is designed and the corresponding equivalent circuit model is developed. Based on the proposed circuit model, the effects of fuzz button degradation on the integrity of both AM signal and PRBS signal are investigated by analysing the metrics such as waveform, eye diagram and bit error rate (BER) of the output signal. In addition, the effects of the carrier frequency of AM signals, and the transmission rate of the PRBS signals on signal transmission are also investigated. The simulation results of the circuit model show good agreements with experimental tests. The research results provide a better understanding regarding the potentially corrosive effects of harsh environments on fuzz button connectors and the negative effects on the signal integrity. Moreover, the research results provide comprehensive data support for identifying key features that are used for the development of machine learning models for fault diagnosis and localisation in radio frequency (RF) circuits with fuzz button interconnections.

Data assimilation is predominantly used for state estimation, combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts, we need good estimates of both the current system state and the model parameters. This article presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three‐dimensional variational (3D‐Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from synthetic aperture radar (SAR) satellite imagery and a ship‐borne survey is shown to significantly improve the predictive capability of the model over a 2‐year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this article considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D‐Var data assimilation algorithm for joint state‐parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation‐based state‐parameter estimation in coastal morphodynamic modelling. Copyright © 2012 Royal Meteorological Society

Telecom-band single nanowire (NW) light-emitting diodes (LEDs) on silicon platforms are promising for use as future on-chip light sources. To realize such an LED, we grew p-i-n junction InP/InAs NWs by the vapor-liquid-solid method and used them to fabricate single NW-LED composites on Si wafers. Using a micro-photoluminescence system and a current injection probe setup, we were able to estimate the optical and electrical properties. In addition, we demonstrated the dynamic signal modulation capabilities of our device through a digital eye diagram with a pseudorandom binary sequence signal. This telecom-band single NW-LED employing Gbps signal modulation has the potential for use as an on-chip light source.

Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances

New Tuning Method for Generalized Predictive Control of the Production of S. cerevisiae

A frequency-hopping communication receiver based on Rydberg atoms, whose carrier frequency changes in accordance with a predetermined sequence, is proposed for what we believe to be the first time and experimentally verified. Based on the coherent population trapping (CPT) effect of atoms, a five-level system is built under the assumption of a constant laser wavelength, where the carrier is the resonance band corresponding to different separated quantum states. Pseudo-random binary sequence (PRBS) and complex image signals undergo keying modulation to achieve frequency-domain hopping reception. In the experiment, a dynamic range of 62 dB is demonstrated. The digital transmission with a data rate of 1 Mbps is performed reliably, within a tunable bandwidth of 50 MHz, an instantaneous bandwidth of 700 kHz and a max hopping rate of 20000 hop/s. The proposed atom-based frequency-hopping receiver is suitable for secure communications, satellite uplinks, quantum cryptography, and high-precision scientific research.

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.

Compared with the well-established techniques for underwater acoustic communication, underwater wireless optical communication provides much higher transmission bandwidth and can be an interesting alternative in short range underwater applications. We experimentally investigated the feasibility of using a directly modulated semiconductor laser as the transmitter in an underwater wireless optical communication (WOC) system. We transmitted pseudo-random binary sequence signals with four different bit rates (5Mbps, 10 Mbps, 15Mbps, 20 Mbps) through three different channels, with eye diagram being plotted and the bit error rate being calculated. No obvious signal distortion induced by water was observed.

Ultrawideband (UWB) signal transmission in Si integrated circuits was performed by use of wireless interconnects. Linear dipole antennas fabricated on a Si substrate showed UWB characteristics in the frequency range of 5-20 GHz. The signal attenuation rates of Gaussian monocycle pulse through 10 Ωcm and 2.29 kΩcm substrates were -0.08 mV/chip and -0.03 mV/chip, respectively. Pseudo random binary signals were transmitted and received between Si chips and the lowest bit error rate of 128 bit binary signal with 13.5 Gbps was 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> . Interference of Gaussian monocycle pulses transmitted from a dipole antenna could be suppressed by use of differential mode signals instead of common mode signals.

On-Line Identification and Steady-State Optimization of Non-Stationary Dynamic Objects

A millimeter wave (MMW) pseudo random code continuous wave (PRC-CW) radar utilizing Doppler compensation is introduced. The novel hybrid transmitted waveform used in this radar combining single frequency pulse with pseudo random binary phase-coded signal is presented which makes consequent target detection through pulse compression independent of Doppler frequency. Doppler frequencies of targets are firstly estimated based on interpolated FFT in the time span of single frequency pulse and the Doppler compensated signal can be constructed. The effect of Doppler mismatch on the correlation detection can be eliminated by processing the compensated signal using conventional pulse compression-FFT, which makes the radar capable of short-range high-speed target detection. The theoretical analysis and simulation result demonstrate the validity of this technique.

In this paper, a novel artificial neural network (ANN) based method dedicated to simultaneously estimating thermal conductivity and thermal diffusivity of CSP (concentrating solar power) plant receiver materials is presented. By monitoring the evolution of these two correlated thermophysical properties during aging cycles, CSP plants' cost efficiency could be maintained. The proposed method is based on the processing of experimental photothermal data using classification and estimation networks. All the networks are feedforward ANN trained with supervised learning algorithms. A pseudo random binary signal (PRBS) is used as excitation and the impact on performance of both the photothermal response length, which is used as model input, and the number of training examples has been evaluated. Of course, the networks' topology has been optimized, allowing the generalization ability to be controlled. Despite the lack of data, the results are promising. Mean relative errors are between 8% and 20%, and the main levers for improvement are identified. In this paper, the study deals with a large range of materials (polymers and metallic alloys).

ABSTRACTThe implementation of functional state approach for modelling of biotechnological processes is considered in this paper. This concept helps in monitoring and control of complex processes such as bioprocesses. Using of functional state modelling approach for biotechnological processes aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters, which complicate the model simulation and parameter estimation. The main advantage of functional state modelling is that the parameters of each local model can be separately estimated from other local models parameters.

Online reinforcement learning for a continuous space system with experimental validation

Parameter estimation is one of the most challenging tasks in large‐scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large‐scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters “saturated hydraulic conductivity” and “field capacity,” which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.