Parameter Extraction Method Research Articles

Fabric-to-cementitious matrix bond behavior in carbon fabric reinforced cementitious matrix (FRCM) composites with added short fibers

Fabric reinforced cementitious matrix (FRCM) composites are promising composite materials with useful properties, such as non-toxicity, fire resistance, corrosion resistance, and vapor permeability. However, the brittle failure mode, fabric efficiency, and tensile strength of FRCM composites require further improvement. Accordingly, the addition of short fibers to the matrix is proposed to improve the mechanical properties of FRCM composites, which are highly influenced by the fabric-to-cementitious matrix bond behavior. The objective of this study is to investigate the fabric-to-cementitious matrix bond behavior in carbon FRCM composites with added short fibers and to propose a method to evaluate the bond parameters. In total, 75 pullout specimens were designed to investigate the effects of short fiber type, short fiber volume content, and embedded length on the fabric-to-cementitious matrix bond behavior using double-sided pullout tests. The experimental results were evaluated in terms of the peak pullout force, displacement at peak pullout force, toughness, average bond strength, and initial stiffness. A closed-form solution was presented for the pullout force-displacement relationship based on a trilinear bond-slip model that accounted for the contribution of weft yarn. Furthermore, a method for the analytical extraction of bond parameters was proposed using the main characteristics of the experimental pullout force-displacement responses.

Parasitic Parameter Extraction and Identification Method for HFT Based on DC-DC Converter in EV Application

Dual active bridge (DAB) resonant converter is one of the most commonly used soft switched converters of the new electric vehicle (EV) energy system. One of the most essential parts of the DAB resonant converter, the high-frequency transformer (HFT) includes certain parasitic parameters that caused voltage and current oscillation, or ring to the DAB converter. These precise parasitic parameters of the HFT provide accurate information and are useful to the DAB converter control system design. However, they are not provided by the manufacturer. This study focuses on this prospect of information, proposing a novel parasitic parameter identification method for the HFT based on DAB converter. Moreover, a 40 kHz full-bridge resonant DAB converter prototype based on SiC MOSFETs and HFPT laboratory prototype is constructed to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results verify that the proposed method is more cost-effective than the conventional way to measure via impedance (IMDP) analyzer, requires shorter simulation time than the traditional finite element analysis (FEA) method, while it is also more accurate has fast convergence than the traditional forgetting factor recursive least square (FFRLS) identification algorithm.

Decomposition and Recognition of English Speech Features Based on Neutrosophic Set Fuzzy Control and Random Matrix Theory

In order to improve the effect of special decomposition and recognition of English speech, based on the idea of neutrosophic set fuzzy control, this paper uses Bayesian method as the basic algorithm of speech recognition to improve the algorithm in combination with English waveform characteristics. Moreover, this paper uses a semi-supervised learning method to process English speech waveform data, collects relevant data through the English speech input system, and then labels the data and obtains a new English speech data set through training and learning. In addition, this paper uses multiple iterations of labeling to obtain the ideal output data, uses neutrosophic set fuzzy control algorithms and machine learning algorithms to perform English speech feature decomposition and recognition, and uses feature parameter extraction methods to perform signal feature extraction. Finally, this article combines the needs of English speech recognition to build a system model and uses simulation tests to perform performance analysis of English speech feature decomposition and recognition model. The results of the research show that the improved algorithm and system model proposed in this paper have relatively good effects.

Degradation process analysis and reliability prediction modeling of hydraulic reciprocating seal based on monitoring data

It is of great significance to analyze the degradation process of the hydraulic reciprocating seal and accurately predict its reliability for taking corresponding measures timely and avoiding losses and accidents. However, the degradation process of a hydraulic reciprocating seal largely depends on actual working parameters of hydraulic cylinder. Therefore, the performance degradation process of the hydraulic reciprocating seal must be studied according to the actual working process parameters of the hydraulic cylinder. In this paper, the degradation process of hydraulic reciprocating seal is studied in a specially designed experimental system. The experimental data are monitored by fiber Bragg grating (FBG) sensor, pressure sensor and friction sensor. The extraction method of performance degradation parameters of the hydraulic reciprocating seal is studied. The reliability prediction model is established, namely R-N curve, which can describe the performance deteriorating process of the hydraulic reciprocating seal.

Sub-THz Small-Signal Equivalent Circuit Model and Parameter Extraction for 3 nm Gate-All-Around Nanosheet Transistor

This paper presents a novel RF small-signal equivalent circuit model and parameter extraction for 3 nm nanosheet gate-all-around field effect transistor (GAAFET). The extrinsic parasitic effect induced by ground-signal-ground (GSG) layout is evaluated by 3D full-wave electromagnetic simulation, and an improved five-step analytical parameter extraction method is proposed for such extrinsic GSG layout. The model parameters for the intrinsic device are analytically determined with the help of nonlinear rational function fitting. The accuracy of the proposed extraction method was confirmed via comparisons between device simulator and electromagnetic simulator with frequency responses up to 300 GHz. Excellent agreement is obtained between the simulated and modeled S-parameters, and the calculated error is lower than 2.689% for the extrinsic layout, and 0.897% for the intrinsic device in the whole frequency range among multi-bias points.

Solar Cell Parameter Extraction Method from Illumination and Dark I-V Characteristics.

A novel method to extract the seven parameters of the double-diode model of solar cells using the current–voltage (I-V) characteristics under illumination and in the dark is presented. The algorithm consists of two subroutines which are alternatively run to adjust all the parameters of the cell in an iterative process. Curve fitting of the light I-V characteristics ensures accuracy in the prediction of the maximum power point, whereas simultaneously fitting the dark I-V characteristics results in a set of physically meaningful parameters that provide information about the physical performance of the photovoltaic devices. Experimental I-V curves of in-house solar cells are used to validate the proposed parameter extraction method, which can be furthermore applied to other types of p–n junction-based photovoltaic devices.

A New Insight and Modeling of Pulse-to-Pulse Variability in Analog Resistive Memory for On-Chip Training

Pulse-to-pulse variability (PPV) is a vital nonideal effect in analog resistive memory, particularly for online training in emerging computing paradigms. The reported PPVs usually show two seemingly contradictory properties: large PPV amplitude between neighboring write pulses but statistically stable switching curves. In this article, a new insight is proposed to unambiguously decouple PPV into two independent components: variability and curve fluctuation. Furthermore, a phenomenological modeling and parameter extraction method is proposed to capture this characteristic. The proposed model can facilitate the exploration of PPV impact on neural network accuracy for online training.

Accurate parametrization revealing an extremely low disorder in polymer field-effect transistors

In this paper, a robust self-consistent parameter extraction method is applied to high-performance p-type printed polymer field-effect transistors. Simultaneous extraction of contact resistance and intrinsic channel mobility with their full gate-voltage dependence is achieved through an analytically reinforced transmission-line method. The proposed method yields a minimum width-normalized contact resistance of 12 kΩ cm and a maximum hole mobility of 1.9 cm2 V−1 s−1. Moreover, the gate-voltage-dependent mobility is interpreted in the framework of trap-and-release transport through double-exponential density of states, unveiling a disorder energy near the transport orbital as low as 29 meV.

Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna

Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.

Josephson junction parameters extraction method for the RCLSJ model

Current-voltage (I-V) characteristics of shunted Nb/Al-AlOx/Nb Josephson junctions (JJs) can be precisely described in the resistive capacitive inductive shunted junction (RCLSJ) model derived from resistive capacitive shunted junction (RCSJ) model with additional parasitic inductance. However, the methods for extraction of current values in the RCLSJ model are not fully developed, which causes unsatisfactor accuracy of the simulations of superconducting integrated circuits. In this work, we propose an innovative method for extraction of parameters that is capable to solve inaccuracy problem. The enhancement is achieved by including the fluctuation of device parameters due to fabrication process in the analysis. A Josephson comparator is designed in the RCLSJ model to test the performance of the proposed parameter extraction method. Obtained results show that our method ensures 2 to 3 times higher simulation accuracy at frequencies above 60 GHz as compared to the traditional method.

A novel hybrid numerical with analytical approach for parameter extraction of photovoltaic modules

• A Novel hybrid approach analytical/Numerical technique is proposed and validated. • The minimum value of ARE is obtained using our model and equal to 1.14%. • The obtained RMSE is 0.0031 A , it’s considered as the smallest among the values found by other model. • Low computational time is carried using our Hybrid approach compared with the others models. • The parameter extraction method is superior in terms of accuracy and convergence. Building an accurate mathematical model of photovoltaic modules is an essential issue for providing reasonable analysis, control and optimization of photovoltaic energy systems. Therefore, this study provides a new accurate model of photovoltaic Panels based on single diode Model. In this case, the proposed model is the link between two models which are the ideal model and the resistance network. All parameters are estimated based on hybrid Analytical/Numerical approach: three parameters photocurrent, reverse saturation current and ideality factor are obtained using an Analytical approach based on the datasheet provided by the manufacturer under Standard Test Conditions. The series and shunt resistances are obtained by using a Numerical approach similar to the Villalva's method in order to achieve the purpose of modeling the resistance network part. Our model is tested with data from the manufacturer of three different technologies namely polycrystalline, Mono-crystalline silicon modules and thin-film based on Copper Indium Diselenide, and for more accurate performance evaluation we are introducing the Average Relative Error and the Root Mean Square Error. The simulated Current-Voltage and Power-Voltage curves are in accordance with experimental characteristics, and there is a strong agreement between the proposed model and the experimental characteristics. The computation time is 0.23 s lower than those obtained using others approach, and all obtained results under real environment conditions are also compared with different models and indicated that the proposed model outperforms the others approach such as villalva’s and kashif’s method.

Comparison of different Classification techniques for the detection of Speech affected due to Respiratory disorders

Respiratory disorders are one of the common chronic disorders across the world. Any malfunctioning in the respiration process affects speech as speech and respiration go hand in hand. Analysis of speech parameter variation due to respiratory disorders is a prime research area in today’s scenario. In this paper, an efficient approach using a machine learning paradigm was used to detect the speech affected due to respiratory disorders. Various speech parameters were extracted (F1, F2, F3… etc.) using PRAAT software along with MFCC and LPC coefficients. Statistically, significant features were obtained to determine the predominant parameters. The extracted features were applied to different machine learning paradigms then different classification techniques were analyzed and compared to distinguish between normal and affected speech.5-fold,10-fold cross-validation, and hold-out data division protocols were used for evaluating the classification results. To evaluate the performance of the proposed methodology, Accuracy, sensitivity, specificity, and the area under receiver operating characteristics (AUC) were used. The result demonstrates that the speech parameters (F1, F2, F3…etc.) extraction method and the LPC method achieved a significantly higher classification accuracy, sensitivity, and specificity of 100% and AUC of 1, under the holdout method. While MFCC method achieved the highest classification accuracy of 83%, sensitivity of 100%, specificity of 67%, and AUC of 0.83.

Noise-Scaled Euclidean Distance: A Metric for Maximum Likelihood Estimation of the PV Model Parameters

This article revisits the objective function (or metric) used in the extraction of photovoltaic (PV) model parameters. A theoretical investigation shows that the widely used current distance (CD) metric does not yield the maximum likelihood estimates (MLE) of the model parameters when there is noise in both voltage and current samples. It demonstrates that the Euclidean distance (ED) should be used instead, when the voltage and current noise powers are equal. For the general case, a new noise-scaled Euclidean distance (NSED) metric is proposed as a weighted variation of ED, which is shown to fetch the MLE of the parameters at any noise conditions. This metric requires the noise ratio (i.e., ratio of the two noise variances) as an additional input, which can be estimated by a new noise estimation (NE) method introduced in this study. One application of the new metric is to employ NSED regression as a follow-up step to existing parameter extraction methods toward fine-tuning of their outputs. Results on synthetic and experimental data show that the so-called NSED regression “add-on” improves the accuracy of five such methods and validate the merits of the NSED metric.

Improved generalized regression neural network for quantitative analysis of crude oil density by gas chromatography

在油气勘探开发领域,快速识别储集层原油性质对于工程技术人员有非常重要的指导意义。地球化学录井技术是用于判断储集层原油性质的常规手段,能为储集层综合评价提供专业认识。该文研究了地化录井中的岩石热解分析和气相色谱分析的原理,提出了一种利用色谱谱图对原油密度进行定量分析的新方法,再结合原油性质密度划分标准,可快速判断储集层原油性质。首先使用计算机图像处理软件对色谱图进行标准化和归一化预处理,并分析了岩石热解气相色谱谱图的曲线特征规律,提出了岩石热解气相色谱谱图的特征参数提取方法,将色谱图转换为特征参数矩阵。其次,研究了3种人工智能预测分类模型,为了适合该实验对储集层原油密度的预测,对其做了部分优化改进,利用元启发式优化算法(麻雀搜索优化算法)对广义回归神经网络的超参数进行了优化,提高了模型的精度和收敛速度。最后,通过现场获得的气相色谱图样本对各模型进行训练并验证,综合对比3种模型预测的结果,发现基于麻雀搜索算法优化的广义回归神经网络预测模型效果最佳,模型稳定,预测密度误差小,可泛化能力强。该研究所提出的方法能为储集层的综合评价研究和现场施工提供可靠的数据支撑。

A reliable parameter extraction method for the augmented GaN high electron mobility transistor small‐signal model

An improved GaN high electron mobility transistor (HEMT) small-signal equivalent model is proposed. To model the effects of extrinsic parameters that vary with the bias voltage, RL and Lds are added to the model. Moreover, substrate-related parameters (Rsub and Csub) are used to model the substrate effect. In order to avoid damaging the gate under high forward gate pressure conditions, an accurate parasitic parameter extraction method is proposed, which improves the reliability and validity of the parameter extraction. The S-parameters of the proposed model are in good agreement with the measured S-parameters in the range of 0.5–20.5 GHz.

Model-based estimation of rail roughness from axle box acceleration

Monitoring rail roughness in the railway network allows directing grinding actions to where they are needed to reduce rolling noise and large wheel/rail forces. To be able to measure rail roughness on a large scale, indirect measurements onboard railway vehicles have to be carried out. Existing methods use either axle box acceleration (ABA) or under-coach noise measurements to monitor the rail roughness indirectly. The two main challenges with rail roughness estimation from vibroacoustic signals measured onboard vehicles are to separate wheel and rail roughness and to take into account varying track dynamics in the railway network. Both questions have not yet been addressed sufficiently.In this paper, an enhanced method for estimating rail roughness from ABA is presented. In contrast to all existing methods in the literature, the presented method operates in the time domain. A time-domain method has the advantage that the spatial variations of roughness become visible and paves the way for the detection of localized defects such as squats or deteriorated welds. The method is based on a previously developed time-domain model for high-frequency wheel/rail interaction and estimates the time series of the roughness from the time series of ABA. In a first step, the time series of the contact force is calculated from the axle box acceleration using a Least Mean Square algorithm for source identification. In a second step, the combined wheel/rail roughness is obtained from the contact force based on a non-linear Hertzian contact model and a convolutional approach to determine wheel and rail displacement. Separation of wheel and rail roughness is possible by cycle-averaging the contact force over a distance corresponding to the wheel perimeter and performing the second step separately for the part of the contact force originating from the wheel and the rail roughness, respectively.The method was tested for simulated ABA obtained from measured wheel and rail roughness. In the relevant wavelength range from 0.5 m to 5 mm, the rail roughness could be estimated with good accuracy for known track dynamics. Overall, deviations in 1/3-octave bands between estimated and actual roughness were below 1 dB. Only for low rail roughness, higher deviations of less than 2.6 dB occurred around the pinned-pinned resonance frequency. Uncertainties in the track parameters affect the roughness estimation, where the most critical parameter is the rail pad stiffness. A deviation of 20% in rail pad stiffness leads to deviations in the rail roughness of up to 3.5 dB in single 1/3-octave bands. The results illustrate the need to extend the method for the simultaneous extraction of track parameters and roughness from measured axle box acceleration.

Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

Grasshopper Optimization Algorithms for Parameter Extraction of Solid Oxide Fuel Cells

The accuracy and reliability of solid oxide fuel cell (SOFC) modeling mainly depend on the precise extraction and optimization of some unknown parameters. However, the SOFC model is a multi-peak, nonlinear, multivariable, and strongly combined system. In the previous decisive optimization methods, it is difficult to achieve satisfactory parameter extraction. Therefore, this article proposes a SOFC parameter extraction method based on the superhuman algorithm and extracts several important parameters of the SOFC model. In addition, the electrochemical model (ECM), which is a typical SOFC model, has also been studied to verify the extraction performance of the glass jump optimization algorithm (GOA) under various working conditions. Simulation results based on MATLAB show that GOA can greatly improve the accuracy, speed, and stability of inferring these unknown parameters through a comprehensive comparison with the particle swarm optimization (PSO) algorithm.

Performance optimization of photovoltaic systems: Reassessment of political optimization with a quantum Nelder-mead functionality

Evaluating the PV system parameters is crucial for the more efficient utilization of sustainable solar energy. Due to PV systems' nonlinear and multimodal nature, efficient evaluation of PV system parameters is still a very challenging task. This paper proposes an improved political optimization algorithm, namely the QNMPO algorithm, which combines quantum rotation gate and Nelder-Mead simplex (NMs). It can be used to evaluate unknown parameters of PV systems and solar cells efficiently. In QNMPO, the quantum rotation gate method can rotate the population of agents to a more favorable position for finding the optimal solution, making the individuals in the middle of the population communicate more frequently and enhancing the population's diversity. In addition, the NMs method can improve the solution quality by better searching the neighborhoods near the optimal solution and then strengthen the advantage of local development of the algorithm so that it can converge to the global optimal solution faster. The developed QNMPO algorithm extracts the unknown parameters for single diode, double diode, triple diode and photovoltaic modules in this study. It is compared with some other representative algorithms. The experimental results show that QNMPO outperforms similar algorithms in convergence speed, solution accuracy, and better solution performance. QNMPO was then used to successfully extract the best parameter values for three real PV models (three commercial PV models (thin-film ST40, monocrystalline SM55, and multi crystalline KC200GT)) at different temperatures and different irradiance levels. The results also show that QNMPO performs consistently and accurately enough under varying temperatures and irradiation. Therefore, the QNMPO algorithm proposed in this paper can be a promising parameter extraction method for solar PV systems with good performance.

Application of Murphy – Good Plot Parameters Extraction Method on Electron Emission from Carbon Fibers

The significance of the Fowler-Nordheim-type plots lies in the possibility of extracting useful and reliable physical parameters of the field electron emitters. This is achieved by the parameter's extraction methods. We report on the application of two parameters extraction methods on field emission data from bundles of carbon fibers (CFs) grouped in nickel tubes and operated inside a typical field emission microscope setup. These methods are the Murphy-Good plot and the conventional Fowler-Nordheim plot iterative method. The physical parameters include the area extraction parameter, the notional (actual) emission area, the formal area efficiency factor and the voltage conversion length. The results obtained from the two methods are discussed and compared to shed light on the controversial nature of these methods. The mechanism of field electron emission is analyzed based on the parameters extracted.