Output Transfer Function Research Articles

Equivalent Circuit Modeling of a Dual-Gate Graphene FET

This paper presents a simple and comprehensive model of a dual-gate graphene field effect transistor (FET). The quantum capacitance and surface potential dependence on the top-gate-to-source voltage were studied for monolayer and bilayer graphene channel by using equivalent circuit modeling. Additionally, the closed-form analytical equations for the drain current and drain-to-source voltage dependence on the drain current were investigated. The distribution of drain current with voltages in three regions (triode, unipolar saturation, and ambipolar) was plotted. The modeling results exhibited better output characteristics, transfer function, and transconductance behavior for GFET compared to FETs. The transconductance estimation as a function of gate voltage for different drain-to-source voltages depicted a proportional relationship; however, with the increase of gate voltage this value tended to decline. In the case of transit frequency response, a decrease in channel length resulted in an increase in transit frequency. The threshold voltage dependence on back-gate-source voltage for different dielectrics demonstrated an inverse relationship between the two. The analytical expressions and their implementation through graphical representation for a bilayer graphene channel will be extended to a multilayer channel in the future to improve the device performance.

Comparison of Basic Inductive Power Transfer Systems With Linear Control Achieving Optimized Efficiency

Inductive power transfer (IPT) systems with front-side and load-side converters are generally developed for better overall system efficiency than a single IPT converter by providing maximum efficiency tracking and output regulation against variations of coupling coefficient ( $k$ ) and load. The optimum efficiency point of an IPT converter is at a particular loading resistance, which varies with $k$ and is hard to measure directly for the purpose of control. Perturb and observe control is normally implemented to iteratively track the optimum efficiency point. However, this heuristic algorithm results in slow response to the variations of $k$ and load. Recently, a much faster linear control for optimum efficiency tracking has been developed for the series-series (SS) IPT system only. This paper proposes a general linear control scheme for all four basic IPT systems to track the optimum efficiency point. Unlike the SS IPT converter, it is found that additional control of either frequency or adaptive compensation is needed for the other three basic IPT converters to operate against the variation of $k$ . Different input to output transfer functions and their $k$ - and load-independent properties at optimum efficiency are identified for all four basic IPT systems. Thus, a general fast linear control can be applied in the front-side converter to achieve optimum efficiency, while the load-side converter regulates the output power independently. Furthermore, the maximum efficiencies of the IPT converters with these four basic compensations for an identical loosely-coupled transformer are compared theoretically and verified experimentally. The parallel–parallel IPT converter gives the best efficiency. Its additional frequency control in conjunction with the optimum efficiency tracking and the output power regulation is also experimentally validated.

Investigation on Dynamic Response of Grid-Tied VSC During Electromechanical Oscillations of Power Systems

This study focuses on the dynamics of a grid-tied voltage source converter (GVSC) during electromechanical oscillations. A small-signal model with GVSC port variables (DC voltage and AC power) as the outputs and a terminal voltage vector as the input is derived to reveal the passive response of the GVSC on the basis of the power equation in the d–q coordinate system. An input–output transfer function matrix is constructed according to the proposed model. The frequency response of this matrix in the electromechanical bandwidth is described to reflect the dynamic behavior of the GVSC. The effects of the operation parameters, i.e., the grid strength, reference value of the control system, and grid voltage, on the dynamic behavior of the GVSC in the electromechanical bandwidth, are investigated using frequency domain sensitivity. Analysis results show that the GVSC generates responses with respect to the electromechanical mode. These responses have different sensitivities to the operation parameters. The IEEE 10-machine power system simulation is performed, and the power hardware-in-the-loop platform with the GVSC was applied to validate the analysis.

Data-driven inference of physical devices: theory and implementation

Given a physical device as a black box, one can in principle fully reconstruct its input–output transfer function by repeatedly feeding different input probes through the device and performing different measurements on the corresponding outputs. However, for such a complete tomographic reconstruction to work, full knowledge of both input probes and output measurements is required. Such an assumption is not only experimentally demanding, but also logically questionable, as it produces a circular argument in which the characterization of unknown devices appears to require other devices to have been already characterized beforehand. Here, we introduce a method to overcome such limitations present in usual tomographic techniques. We show that, even without any knowledge about the tomographic apparatus, it is still possible to infer the unknown device to a high degree of precision, solely relying on the observed data. This is achieved by employing a criterion that singles out the minimal explanation compatible with the observed data. Our method, that can be seen as a data-driven analog of tomography, is solved analytically and implemented as an algorithm for the learning of qubit channels.

Application of Artificial Neural Network in Modeling separation of microalgae

The economization and commercialization of the bio fuels production from microalgae require solving some problems facing it. One of the most important and expensive stages is the separation of microalgae from the medium culture. The modelling of Separation biological processes can be used as a safe tool to save the economy and avoid repeated testing. Among the methods of modelling, artificial neural network is accurate and widely used in biotechnological processes. Results of the study showed that correlation coefficient reached 1 indicating that there is a good match between actual values and those predicted by modelling. From a total of 18 data, two-thirds of data was used to train a network and the remaining third was used to assess the modelling accuracy. The middle transition function purelin , output transfer function tansig and the number of neurons (five) were determined as the best parameters to train the network. The error rate of network training was estimated to be 0.0511 and error evaluation of the network accuracy was found to be 0.992.

Modeling and Analysis of On-Chip Power Noise Induced by an On-Chip Linear Voltage Regulator Module With a High-Speed Output Buffer

In this paper, analytical models of on-chip power noise induced by an on-chip linear voltage regulator module (VRM) circuit with a high-speed output buffer are proposed. Based on the piecewise linear approximated mosfet I – V curve model, closed-form equations for the on-chip power noise induced by an on-chip low-dropout regulator are derived. The accuracy of the proposed analytical model is validated by SPICE simulation with a 110-nm CMOS technology library. Based on the proposed analytical models, the impacts of VRM design parameters on VRM output noise induced by load current and external noises are analyzed. Because self-impedance at the VRM output and external noise transfer functions share a common resonant frequency, the on-chip power noise is minimized by avoiding the resonant frequency from peak frequencies of noise source spectrums. The larger on-chip decoupling capacitance at load reduces, the overall on-chip VRM output noise. While the larger pass transistor size reduces the on-chip VRM output noise induced by the reference voltage fluctuation, it increases the noise generated by off-chip power fluctuation. The reference voltage node needs to be carefully designed, as opposed to an off-chip power distribution network, due to its dominant impact on the on-chip VRM output noise. The analysis results based on the proposed model provide an in-depth understanding of and useful design guidance for on-chip power noise induced by the on-chip linear VRM with a high-speed output buffer.

Design and modeling of improved controller using DC source fed permanent magnet synchronous motor drive with enhanced DC-DC converter by reducing vibrations for industrial applications

The proposed research involves Design and Modeling of Improved Controller (PID), Using DC Source Fed Permanent Magnet Synchronous Motor Drive with Enhanced DC-DC Converter by reducing vibration for Industrial Applications. It consists of a DC-DC converter, voltage-link module, fractional second order system PID controller. In this proposed improved controller, enhanced converter, DC-link switching is achieved by a bridged ripple voltage which results in the improved quality of output power, and it also reduces vibration, and noise in the drive without affecting mechanical properties. Reduction of switches makes the system more cost effective. A simulation of fractional second-order function, Buck-Boost converter is designed, and its concert is analyzed for various functioning factor conditions. The Three-Port Converter and B4-Inverter fed Permanent Magnet Synchronous Motor Drive focused on the industrial applications. An Ampere-hour unit powered converter is used at inductor terminal, which has been realized by minimizing the distortion and decreasing the Ampere-hour unit rating a new topology is proposed. As Comparing with previous system, the proposed system results in the reduced voltage fluctuation at the switches. Hence the proposed system proves power switches are compact, with reduced inrush current and improved output transfer function. Based on this a fulfilling closed loop validation has been performed for both simulation and experimental arrangement.

A New Hybrid Inductor-Based Boost DC-DC Converter Suitable for Applications in Photovoltaic Systems

A new hybrid step-up converter suitable in applications where large conversion ratios are needed is presented. The topology is still simple, containing only one transistor and three diodes. A detailed dc and ac analysis is performed and all design equations are provided. Compared to other topologies of the same type, the proposed converter exhibits lower or equal current and voltage stresses. A state space model is provided including the conduction losses and based on it the audio susceptibility and the control to output function are derived. As the converter is still of second order with the control to output transfer function exhibiting a right half plane zero, controller design is practically the same like in a Boost topology. It is shown how the proposed converter can be used in a photovoltaic system for performing the maximum power point tracking algorithm using the perturb and observe method. All theoretical considerations are verified through simulation and finally validated by practical experiments.

A Decision-Making Algorithm for Energo-Economic Sustainability and Efficiency in Buildings: A Case Study in Turkey

Today, sustainability and energy efficiency are of prime importance in satisfaction of thermal and electrical loads of buildings. In this study, innovative hybrid solutions alternative to conventional HVAC systems are investigated. Objective of study is to minimize the payback period and CO2 emissions are main objectives. For conventional HVAC systems two sources of energy, namely natural gas and electricity were considered as the base line. Energy sources for the innovative methods were considered to be solar energy, ground heat and waste heat. Conventional system was considered to be a backup system when innovative energy resources are insufficient. Hourly heating, cooling and electrical power loads of Eser Green Building, which already has LEED Platinum certificate were used for the case study, which aims to further improve the energy and exergy efficiency. In the new algorithm being developed, all power conversion systems were defined in a simple input- output transfer function format. A decision-making algorithm and an ExcelTM-based simulation program were developed and tried with Eser Green Building input data for different renewable energy source and system combination conventional systems, energy sources, and equipment in term of investment, operation and total cost, payback period, and carbon dioxide emission values. Payback period of Eser green building is 11,8 years and for two hybrid systems are 13,2 years and 9,2 years, respectively. Carbon dioxide emissions by hybrid systems under the same load conditions were found to be 488 kgCO2/h and 592 kgCO2/h for approaches, respectively.

Analysis of Frequency Characteristics of the Half-Bridge CLCL Converter and Derivative Topologies

CLCL resonant converters represent an interesting and emerging high-order resonant converter topology. In this paper, some modeling methods have been introduced and analyzed in detail, with special emphasis on their frequency characteristics. Hence, a small-signal model has been set up and a control to output transfer function has been obtained by using the state-space method. Also, frequency characteristics related to different component parameters is investigated and a systematic modeling approach for derivative topologies with higher order resonant tanks is proposed. The relevance of the obtained results are further demonstrated through experimental tests.

Large-scale dynamic modeling of task-fMRI signals via subspace system identification

Objective. We analyze task-based fMRI time series to produce large-scale dynamical models that are capable of approximating the observed signal with good accuracy. Approach. We extend subspace system identification methods for deterministic and stochastic state-space models with external inputs. The dynamic behavior of the generated models is characterized using control-theoretic analysis tools. To validate their effectiveness, we perform a probabilistic inversion of the identified input–output relationships via joint state-input maximum likelihood estimation. Our experimental setup explores a large dataset generated using state-of-the-art acquisition and pre-processing methods from the Human Connectome Project. Main results. We analyze both anatomically parcellated and spatially dense time series, and propose an efficient algorithm to address the high-dimensional optimization problem resulting from the latter. Our results enable the quantification of input–output transfer functions between each task condition and each region of the cortex, as exemplified by a motor task. Further, the identified models produce impulse response functions between task conditions and cortical regions that are compatible with typical hemodynamic response functions. We then extend subspace methods to account for multi-subject experimental configurations, identifying models that capture common dynamical characteristics across subjects. Finally, we show that system inversion via maximum-likelihood allows the time-of-occurrence of the task stimuli to be estimated from the observed outputs. Significance. The ability to produce dynamical input–output models might have an impact in the expanding field of neurofeedback. In particular, the models we produce allow the partial quantification of the effect of external task-related inputs on the metabolic response of the brain, conditioned on its current state. Such a notion provides a basis for leveraging control-theoretic approaches to neuromodulation and self-regulation in therapeutic applications.

Sequences Domain Impedance Modeling of Three-Phase Grid-Connected Converter Using Harmonic Transfer Matrices

The sequence-domain impedance using harmonic linearization technique can well describe the harmonic behavior of the interconnected system consisting of the grid and converter. However, fast phase-locked loop (PLL) design, unbalanced current controller structure will cause the unneglectable off-diagonal components in impedance matrix of converter. Due to the frequency coupling and sequence coupling effect in the sequence domain, the traditional single input single output transfer function lacks the capability to accurately express this frequency shift relationship. Therefore, in this paper, the harmonic transfer matrix (HTM) is used to model the frequency shift existing in the sequence-domain impedance model. By unifying positive sequence and negative sequence components, the complexity of impedance model derivation can be reduced. The linear time-periodic system is transformed to a linear time-invariant system in harmonic domain. And then, Nyquist criteria is applied to analyze the stability of the interconnected system. Finally, simulation results verify the proposed HTM-based sequence-domain impedance modeling method.

Ozone Hole Area Prediction at Earth's North and South Poles by Marvel Interface

This paper explores the possibility of predicting ozone hole area (Maximum Area) at North and South Pole usingArtificial Neural Network (ANN) and then developing the forecasting network by using Graphical User Interface (GUI)named MARVEL. Two models are designed for predictions: a) Ozone hole area prediction at North Pole and b)Ozone hole area prediction at South Pole. For both the models, the number of input parameters is taken as year,month, date, sunspot area, sunspot number and solar mean magnetic field. Here, more than 35 years of data isused for training purpose and then predictions are made from November 23, 2015 to September 30, 2016.Forecasting network (MARVEL) is developed to imbibe the properties of ANN. It can get trained with the most recentdata accessible to the user and then making future predictions for short (one day) and long term (months, years)durations, respectively. From the results, Mean Square Error (MSE) for Model 1 and Model 2 is found to be 6.7166DU and 0.3582 DU, respectively. It can be concluded that with 30 numbers of neurons and input and output transferfunctions as Tangent Sigmoid and pure linear, along with one hidden layer, the forecasting network predictions areplausible and appreciably close to actual observed values. It is to be noted that the change of ozone hole area atpoles have dynamic reasons behind it and sun parameters are not responsible for that. This paper is an attempt topresent the application of Artificial Neural Network of connecting the unrelated parameters and processe

A Modified Frequency Domain Condition for the Physical Realizability of Linear Quantum Stochastic Systems

This paper is concerned with a modified version of the frequency domain physical realizability (PR) condition for linear quantum systems. We consider open quantum systems whose dynamic variables satisfy the canonical commutation relations of an open quantum harmonic oscillator and are governed by linear quantum stochastic differential equations (QSDEs). In order to correspond to physical quantum systems, these QSDEs must satisfy PR conditions. We provide a relatively simple proof that the PR condition is equivalent to the frequency domain $(J,J)$ -unitarity of the input–output transfer function and orthogonality of the feedthrough matrix of the system without the technical spectral assumptions required in previous work. We also show that the poles and transmission zeros associated with the transfer function of PR linear quantum systems are the mirror reflections of each other about the imaginary axis. An example is provided to illustrate the results.

All-optical regenerator of multi-channel signals

One of the main reasons why nonlinear-optical signal processing (regeneration, logic, etc.) has not yet become a practical alternative to electronic processing is that the all-optical elements with nonlinear input–output relationship have remained inherently single-channel devices (just like their electronic counterparts) and, hence, cannot fully utilise the parallel processing potential of optical fibres and amplifiers. The nonlinear input–output transfer function requires strong optical nonlinearity, e.g. self-phase modulation, which, for fundamental reasons, is always accompanied by cross-phase modulation and four-wave mixing. In processing multiple wavelength-division-multiplexing channels, large cross-phase modulation and four-wave mixing crosstalks among the channels destroy signal quality. Here we describe a solution to this problem: an optical signal processor employing a group-delay-managed nonlinear medium where strong self-phase modulation is achieved without such nonlinear crosstalk. We demonstrate, for the first time to our knowledge, simultaneous all-optical regeneration of up to 16 wavelength-division-multiplexing channels by one device. This multi-channel concept can be extended to other nonlinear-optical processing schemes.

Uncertainty Quantification of Microstructure-Governed Properties of Polysilicon MEMS.

In this paper, we investigate the stochastic effects of the microstructure of polysilicon films on the overall response of microelectromechanical systems (MEMS). A device for on-chip testing has been purposely designed so as to maximize, in compliance with the production process, its sensitivity to fluctuations of the microstructural properties; as a side effect, its sensitivity to geometrical imperfections linked to the etching process has also been enhanced. A reduced-order, coupled electromechanical model of the device is developed and an identification procedure, based on a genetic algorithm, is finally adopted to tune the parameters ruling microstructural and geometrical uncertainties. Besides an initial geometrical imperfection that can be considered specimen-dependent due to its scattering, the proposed procedure has allowed identifying an average value of the effective polysilicon Young’s modulus amounting to 140 GPa, and of the over-etch depth with respect to the target geometry layout amounting to m. The procedure has been therefore shown to be able to assess how the studied stochastic effects are linked to the scattering of the measured input–output transfer function of the device under standard working conditions. With a continuous trend in miniaturization induced by the mass production of MEMS, this study can provide information on how to handle the foreseen growth of such scattering.

Artificial neural network modeling for removal of azo dye from aqueous solutions by Ti anode coated with multiwall carbon nanotubes

In this study, a titanium based electrode, coated with multiwall carbon nanotubes (MWCNTs/Ti) was applied as anode in a laboratory‐made electrochemical reactor. The MWCNTs/Ti electrode was prepared by the electrophoretic deposition (EPD) method in an aqueous solution and was characterized by field emission scanning electron microscopy. The prepared electrode was used as anode in decolorization of C.I. Acid Red 33 as typical target pollutant in aqueous solutions. Effect of pH, current density, and reaction time was evaluated on color removal efficiency. The experimental studies revealed that color removal efficiency and TOC removal efficiency were 90 and 15%, respectively, at optimum conditions: pH = 8, current density of 5.5 mA/cm2 and reaction time of 60 min. Also, the electrochemical decolorization was modeled using a three‐layered feed‐forward back propagation artificial neural network (ANN), consisting of “trainbfg” as learning algorithm, “tansig” transfer function in the hidden layer with 10 neuron and “purelin” as output transfer function in order to predict the color removal efficiency. Comparison between the predicted values and selective experimental data showed that the developed ANN model has a high correlation coefficient (R2) of 0.995 and can predict decolorization efficiency with acceptable accuracy. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1778–1784, 2017

Switching Flow Graph Modelling of RRPP Based DC-DC Converter

Objective: Small-signal modelling is a widespread analysis practice in power electronic converters which is used to fairly accurate the performance of non-linear devices with linear equations. The order of the dc-dc depends on the non-linear elements of the system. As the number of non-linear element of the converter is high, it’s modelling and control becomes extremely complicated. Thus, there is a need for generalization of such converters by small signal modelling. Traditional modelling schemes using the averaging and small-signal linearization procedures are utilised for the advance modelling of the converters. Method: In this work, the purpose of the switching flow graph (SFG) and the Mason’s gain formula aid the solution of the equations. The duty cycle to output and the input to output transfer functions are obtained. Those transfer function can be used for closed loop examination. Findings: Open loop analysis of the converter is done and the outcomes are verified through simulation. Theoretical and simulation results are shown to prove the accuracy of the developed small signal models. The derived topology is unstable in open loop condition. A prototype is developed to validate the performance of the proposed topology. Improvement: Closed loop analysis can be carried out with any type of controller.

Input output transfer function model development for a prediction of cyanobacteria cell number in Youngsan River

최근의 우리나라 수계에서의 하천에서의 조류 대번성은 심각한 사회 환경적 문제가 되고 있다. 이중 독성이 강한 남조류의 발현은 수생태계의 건강성과 안전한 물공급에 위협이 될 수 있다. 영산강 수계의 승촌보와 죽산보 지점의 남조류 세포수와 환경인자간의 인과관계 분석을 위해 선백색화 시계열간의 배타적 상관분석을 수행하였고 이를 기반으로 이들 사이의 입출력 모형을 도출하였다. 입출력 모형의 겨울철 남조류 세포수 반응 특성을 고려하기 위해서 수온의 문턱거동을 도입하였고, 모형의 남조류 세포수에 대한 설명력을 증가시키는 효과를 얻었다. 입출력 모형의 남조류 세포수의 모의능이 완전하진 않으나, 비교적 간단한 구조를 가진 입출력 모형의 구조는 모형 적용의 용이성이 높은 것으로 판단된다.

Using the sliding‐mode control approach for analysis and design of the boost inverter

A cascade control strategy is employed in the boost inverter to generate a sinusoidal signal at grid frequency with very low distortion. The internal loop of the cascade employs a switching surface based on the difference of the inductor currents to induce sliding motions in the power stage. The outer loop in turn establishes the reference of the internal loop and ensures the tracking of an external sinusoidal signal at 50 Hz. The reported approach is analytical and is based on the equivalent control method. The root locus of the capacitor voltages at the equilibrium point of the inner loop is obtained assuming a constant value of the loop reference. In the particular case of a zero reference, sinusoidal variations at grid frequency are superposed to the corresponding equilibrium point and the resulting ideal dynamics are linearised yielding the control to output transfer function of the system. A proportional-integral (PI) compensator is designed for both large bandwidth and small phase error. Experimental results in a 500 W prototype are in perfect agreement with the analytical predictions.