Parameter Extraction Process Research Articles

Optimization of thin electric propeller using physics-based surrogate model with space mapping

The propulsion system greatly effects the performance of small electric powered Unmanned Aerial Vehicles (UAV). The propeller is one of the key elements of an electric propulsion system. The present research aims to optimize the propeller shape by utilizing the physics-based surrogate modeling strategy along with a space mapping method. A quasi-global construction is made that perceptively links a low-fidelity (coarse) model and a high-fidelity (fine) model, thus avoiding the direct optimization of the fine model. The space mapping utilizes a parameter extraction process that assist the surrogate model to locally realign itself with the fine model. For coarse model, an open source propeller analyses and design program (QPROP) is used. This program uses an extension of the classical blade element formulation. The fine model is based on the computational fluid dynamics analysis utilizing the finite-volume discretization of the Reynolds-averaged Navier–Stokes equations. The computed results have been verified by comparing them with the available propeller performance data. After a successful validation, geometric parameters for the propeller are varied to study their effect on performance and carry out parametric sensitivity analysis. The selected design variables control the section airfoil shape and the propeller geometry. The optimized design has been achieved with few iterations of the fine model reducing the overall computational cost of optimization process. The results indicate that the space mapping surrogate modeling is an effective strategy for propeller shape optimization.

Self-starting VCSEL-based optical frequency comb generator.

In this paper, we present the simulation and experimental results of the first closed-loop system based on a directly modulated VCSEL in a gain-switching condition to generate optical frequency combs (OFC). In order to simulate the self-starting VCSEL-based optical frequency comb generator (SVOFC), we applied an intrinsic parameter extraction process to a C-band VCSEL using laser rate equations, static and dynamic measurements, and equivalent circuit models. The widest (62 GHz) and flattest (0.8) simulated OFC is obtained when the repetition frequency f0 is 2.5 GHz. Implementation of the C-band SVOFC also shows that under constant electrical conditions, flatness higher than 0.85 and spectral widths of 50 GHz are obtained when f0 = 2.5 GHz. The lowest phase noise at 10 kHz from the extracted electrical carrier is -127 dBc/Hz and is obtained when the optical fiber length is 5 km and f0 = 1.25 GHz.

Parameter extraction of solar cell using intelligent grey wolf optimizer

The focus of power producers has shifted from conventional energy sources to sustainable energy sources because of the depletion of fossil fuels and carbon emission causing global warming and climate change. Solar cells are the most prominent option to deal with these problems. The precise estimation of solar cell parameters is very much required before their installation to achieve high efficiency. In recent years applications of several optimization algorithms for parameter estimation of the solar cell have been addressed. Recently, intelligent grey wolf optimizer (IGWO), which is an advanced version of grey wolf optimizer (GWO) incorporating a sinusoidal truncated function as a bridging mechanism and opposition based learning has been introduced. The wide applicability of this variant has been examined over different conventional benchmark functions and on some real problems. This fact motivated authors to employ this variant on parameter extraction process. The main motivation behind the implementation of IGWO on solar cell parameter estimation process is the efficiency of this version to deal with complex optimization problems. To estimate the PV cell parameter values, measurement of voltage and current are considered at three important points. These are open circuit point, short circuit point and maximum power point, for two solar cell representative models i.e. single diode model and double diode model. Results of IGWO are compared with the results of other variants of GWO on these two models and for three films (Mono crystalline, poly crystalline and thin film). Results reveal that IGWO produces better results.

Robust Fast Electromagnetic Optimization of SIW Filters Using Model-Based Deviation Estimation and Jacobian Matrix Update

In this paper, a simple fast electromagnetic (EM) optimization method for substrate integrated waveguide (SIW) filters is presented. The feature parameter deviations are estimated by direct response approximation based on the feature parameter model with few times quasi-Newton method. The parameter extraction process is more concise than the parameter identification techniques based on the rational polynomial model and can be used for lossless networks as well as lossy networks. The dimension correction is based on the Jacobian matrix which relates the physical dimensions and the feature parameters. An analytical general mapping model for SIW filters is extracted and applied to generate and update the Jacobian matrix for the first time. The extracted model can be directly used for SIW filter design without repeated extraction. This helps to minimize the number of EM simulations and avoid extra work for model extraction. Using general mapping model can overcome the robustness issue introduced by Broyden update methods. The number of EM simulations required to meet the specifications can be significantly reduced. The effectivity and robustness of the proposed method is validated by two design examples: a 10-order SIW Chebyshev filter and a 5-order SIW cross-coupled filter. The proposed method exhibits low complexity, good stability, good convergence speed and the ability to avoid being trapped in local minimum.

A Novel Measurement Method of Mechanical Properties for Individual Layers in Multilayered Thin Films.

Various multilayered thin films are extensively used as the basic component of some micro-electro-mechanical systems, requiring an efficient measurement method for material parameters, such as Young’s modulus, residual stress, etc. This paper developed a novel measurement method to extract the Young’s moduli and residual stresses for individual layers in multilayered thin films, based on the first resonance frequency measurements of both cantilever beams and doubly-clamped beams. The fabrication process of the test structure, the corresponding modeling and the material parameter extraction process are introduced. To verify this method, the test structures with gold/polysilicon bilayer beams are fabricated and tested. The obtained Young’s moduli of polysilicon films are from 151.38 GPa to 154.93 GPa, and the obtained Young’s moduli of gold films are from 70.72 GPa to 75.34 GPa. The obtained residual stresses of polysilicon films are from −14.86 MPa to −13.11 MPa (compressive stress), and the obtained residual stresses of gold films are from 16.27 to 23.95 MPa (tensile stress). The extracted parameters are within the reasonable ranges, compared with the available results or the results obtained by other test methods.

A Datasheet Driven Unified Si/SiC Compact IGBT Model for N-Channel and P-Channel Devices

This paper presents a unified physics-based insulated-gate bipolar transistor (IGBT) compact model for circuit simulation that predicts the performance of both Si and SiC, n- and p-channel devices. The model can predict the detailed switching waveforms of these technologies based on its charge-based formulation. Further, this compact IGBT model is presented alongside a unique datasheet-driven parameter extraction process. The parameter extraction process enables users to quickly extract model parameters from data typically published without the need of taking physical measurements. The model has been validated with both Si and SiC devices for static and dynamic characteristics. The SiC IGBTs used for validation are a 12.5-kV n-channel device and a 13-kV p-channel device, while the Si IGBT chosen was IXDH30N120 from IXYS Corp. (Milpitas, CA, USA). This is the only IGBT model that predicts the performance of both n- and p-channel, Si and SiC devices, providing more freedom for the development of complex power electronics circuit designs. The convergence of the model has been verified by implementing a complex circuit consisting of both a dc-dc converter and a dc-ac inverter. The results presented here show that the unified model can be used to describe the behavior of a wide range of Si and SiC IGBT circuits. This paper is accompanied by a Verilog-A source code and a power point file demonstrating the model parameter extraction sequence.

Silicon Carbide BJT Oscillator Design Using S-Parameters

Radio frequency (RF) oscillator design typically requires large-signal, high-frequency simulation models for the transistors. The development of such models is generally difficult and time consuming due to a large number of measurements needed for parameter extraction. The situation is further aggravated as the parameter extraction process has to be repeated at multiple temperature points in order to design a wide-temperature range oscillator. To circumvent this modelling effort, an alternative small-signal, S-parameter based design method can be employed directly without going into complex parameter extraction and model fitting process. This method is demonstrated through design and prototyping a 58 MHz, high-temperature (HT) oscillator, based on an in-house 4H-SiC BJT. The BJT at elevated temperature (up to 300 0C) was accessed by on-wafer probing and connected by RF-cables to the rest of circuit passives, which were kept at room temperature (RT).

Extraction of Intrinsic Parameters of Lead–Acid Batteries Using Energy Recycling Technique

This paper presents the use of an energy recycling technique to extract the intrinsic parameters of lead–acid batteries. The charging and discharging currents of the battery under test are programmed by controlling a bidirectional dc–dc converter to profile the power flow between the battery and a supercapacitor. The sampled battery voltage and current information over the charging and discharging periods are used to estimate the parameters of a high-order electrical battery model with a modified particle swarm optimization algorithm and compare with the battery voltage and current predicted with the extracted parameters. With the energy recycling mechanism, the proposed parameter extraction process is environment friendly and has low power dissipation, thus increasing the power handling density and allowing long testing duration. A prototype that can extract the intrinsic parameters of eight different types of 12 V, 130 A lead–acid batteries has been built. Its performance has been evaluated with different charging and discharging profiles. The estimated parameters are favorably verified with the theoretical predictions, results obtained by the extended Kalman filter method, and results obtained on a calibrated commercial battery testing system. Results reveal that testing batteries with both charging and discharging processes gives a more accurate prediction of battery performance.

Frequency‐dependent empirical modeling for intrinsic output capacitance and conductance of HR PD‐SOI MOSFETs

AbstractA high resistivity PD‐SOI MOSFET equivalent circuit model including a parallel connection of intrinsic output capacitance and conductance with frequency‐dependent empirical equations is newly proposed to accurately simulate their abrupt change in the low‐frequency region. This new empirical model is superior to a conventional RC one, because equivalent circuit structure and parameter extraction process are simpler. Better agreements with measured Y22‐parameter are obtained for the new model than the conventional one.

Primary central nervous system lymphoma and glioblastoma image differentiation based on sparse representation system

It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.”

OPTIMIZATION OF BETA-SITOSTEROL EXTRACTION FROM LEUCAENALEUCOCHEPALA BY MICROWAVE ASSISTED HYDRODISTILLATION

Microwave-assisted hydrodistillatlion (MAH) extraction process can extract phytosterol (beta-sitosterol) from Leucaena leucochepala. The suitable parameter of the extraction process to extract the highest yield of phytosterol srom a plant can be optimized by using central composit design (CCCD). Two parameters extraction process were studied which are duration of time (4 to 8 minutes) and solvent ethanol concentration (55%-95%) to gain the highest yield of extracted beta-sitosterol from Leucaena leucochepala. The Liebermann-Burchard analysis was used to analyse phytosterol content in the extracted from the samples. The CCD model parameter was significant because of the values for Prob>F is less tahn 0.0500 which are 0.006 and value lack of fit F-value which is 5.44 represent only 6.77% chances to occur failure. This indicated the CCD with duration of extraction and the ethanol solvent concentration is the optimum parameter for the extraction of beta-sitosterol (phytosterol) from Leucaena leucochepala legume pod with the highest yield of phytosterol of 0.2717 mg/mL at 75% ethanol and 6 minutes.

Model parameter extraction of lateral propagating surface acoustic waves with coupling on SiO2/grating/LiNbO3 structure

This paper investigates how lateral propagation of Rayleigh and shear horizontal (SH) surface acoustic waves (SAWs) changes with rotation angle θ and SiO2 and electrode thicknesses, hSiO2 and hCu, respectively. The extended thin plate model is used for purpose. First, the extraction method is presented for determining parameters appearing in the extended thin plate model. Then, the model parameters are expressed in polynomials in terms of hSiO2, hCu, and θ. Finally, a piston mode structure without phase shifters is designed using the extracted parameters. The possible piston mode structures can be searched automatically by use of the polynomial expression. The resonance characteristics are analyzed by both the extended thin plate model and three-dimensional (3D) finite element method (FEM). Agreement between the results of both methods confirms validity and effectiveness of the parameter extraction process and the design technique.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Multiple operating frequency selections for reconfigurable antenna design by SM based optimisation

In surrogate-based optimisation methods, computationally burden yet precise fine model is utilised alongside less accurate however quick coarse model to decrease the general computational exertion. Surrogate-based optimisation methods are firstly applied to reconfigurable antenna design problems in this work. Space mapping (SM) with inverse difference method enables productive procedure to decrease computational exertion while enhancing the convergence. Inverse difference mapping is constituted by difference knowledge that is obtained by input and output of the problem space. In addition, inverse coarse model that is obtained by feed forward multi-layer perceptron can generate necessary knowledge to form inverse mapping. This mapping is used to eliminate the direct optimisation-based parameter extraction process. The efficiency of SM with inverse difference technique will be demonstrated by reconfigurable antenna design examples in terms of their convergence and accuracy. Moreover, this technique will be compared to aggressive SM with regard to the convergence efficiency.

Reducing the Complexity of Dataflow Graphs Using Slack-Based Merging

There exist many dataflow applications with timing constraints that require real-time guarantees on safe execution without violating their deadlines. Extraction of timing parameters (offsets, deadlines, periods) from these applications enables the use of real-time scheduling and analysis techniques, and provides guarantees on satisfying timing constraints. However, existing extraction techniques require the transformation of the dataflow application from highly expressive dataflow computational models, for example, Synchronous Dataflow (SDF) and Cyclo-Static Dataflow (CSDF) to Homogeneous Synchronous Dataflow (HSDF). This transformation can lead to an exponential increase in the size of the application graph that significantly increases the runtime of the analysis. In this article, we address this problem by proposing an offline heuristic algorithm called slack-based merging . The algorithm is a novel graph reduction technique that helps in speeding up the process of timing parameter extraction and finding a feasible real-time schedule, thereby reducing the overall design time of the real-time system. It uses two main concepts: (a) the difference between the worst-case execution time of the SDF graph’s firings and its timing constraints (slack) to merge firings together and generate a reduced-size HSDF graph, and (b) the novel concept of merging called safe merge , which is a merge operation that we formally prove cannot cause a live HSDF graph to deadlock. The results show that the reduced graph (1) respects the throughput and latency constraints of the original application graph and (2) typically speeds up the process of extracting timing parameters and finding a feasible real-time schedule for real-time dataflow applications. They also show that when the throughput constraint is relaxed with respect to the maximal throughput of the graph, the merging algorithm is able to achieve a larger reduction in graph size, which in turn results in a larger speedup of the real-time scheduling algorithms.

Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm

As a result of the wide prevalence of using the multi-crystalline silicon solar cells, an accurate mathematical model for these cells has become an important issue. Therefore, a three diode model is proposed as a more precise model to meet the relatively complicated physical behavior of the multi-crystalline silicon solar cells. The performance of this model is compared to the performance of both the double diode and the modified double diode models of the same cell/module. Therefore, there is a persistent need to keep searching for a more accurate optimization algorithm to estimate the more complicated models’ parameters. Hence, a proper optimization algorithm which is called Moth-Flame Optimizer (MFO), is proposed as a new optimization algorithm for the parameter extraction process of the three tested models based on data measured at laboratory and other data reported at previous literature. To verify the performance of the suggested technique, its results are compared with the results of the most recent and powerful techniques in the literature such as Hybrid Evolutionary (DEIM) and Flower Pollination (FPA) algorithms. Furthermore, evaluation analysis is performed for the three algorithms of the selected models at different environmental conditions. The results show that, MFO algorithm achieves the least Root Mean Square Error (RMSE), Mean Bias Error (MBE), Absolute Error at the Maximum Power Point (AEMPP) and best Coefficient of Determination. In addition, MFO is reaching to the optimal solution with the shortest execution time when it is compared with the other tested algorithms.

A Comprehensive and Accurate Analytical SPAD Model for Circuit Simulation

The single-photon avalanche diode (SPAD) is an attractive photosensor due to its high sensitivity and low dead time. In this paper, a comprehensive, accurate analytical SPAD circuit simulation model is proposed and implemented in Verilog-A hardware description language. It shows great application universality and is fully compatible with mainstream commercial circuit simulators. This model incorporates all important operating features of the SPAD. Most importantly, to the best of our knowledge, it is the first time that the band-to-band tunneling mechanism and the temporal dependence of after-pulsing probability are included in an SPAD circuit simulation model. The parameter extraction processes from interavalanche time measurement of a free-running SPAD device are discussed. The simulation results indicate that the proposed model works well. In addition, the primary dark counts from the model are validated against the measurement results. A maximum relative error of 8.7% is observed at 20 °C with an excess voltage of 0.5 V. The accuracy of this paper can be greatly improved by using more accurate physical parameters available from the SPAD fabrication vendor.

Calibration-free 2f WMS with in situ real-time laser characterization and 2f RAM nulling.

This Letter demonstrates a new calibration-free 2f wavelength modulation spectroscopy (WMS) technique to measure gas concentration and pressure without the need for laser precharacterization. A 1650-nm laser diode is used for methane concentration and pressure measurements for pressures up to 4 bar and for a modulation index (m) of 2.2. All laser parameters such as the intensity, linear and nonlinear intensity modulation (IM), frequency modulation (FM) characteristics, the phase difference ψ1 between the FM and the linear IM, and the phase difference ψ2 between the FM and the nonlinear IM are accurately estimated in situ and in real time. This technique accounts for variations in these parameters that arise due to scanning of the laser's center wavelength, laser temperature variations, and aging of the laser. The laser is modulated at its phase quadrature frequency at which the linear IM and the FM are orthogonal to each other (ψ1=90°). This ensures that the two linear IM-dependent distorting Fourier components are orthogonal to the detection axis, and the undistorted 2f signal is recovered. This simplifies the simulation and gas parameter-extraction process. Finally, 2f RAM nulling is implemented to remove the significant absorption-independent 2f residual amplitude-modulation (RAM) signal that is seen to cause significant distortion of the 2f signal and detector saturation.

Flower Pollination Algorithm based solar PV parameter estimation

Developing a highly accurate simulation technique for Photovoltaic (PV) systems prior to the installation is very important to increase the overall efficiency of using such systems. Providing a more accurate optimization algorithm to extract the optimal parameters of the PV models is therefore continuously required. Flower Pollination Algorithm (FPA) is proposed as a new optimization method to extract the optimal parameters of a single diode and a double diode models. The proposed extraction technique is tested using three different sources of data. The first source is the data reported in the previous literature, while the second source is the experimental data measured at the laboratory. The third source is the experimental data obtained from the data sheets of different types of solar modules. The FPA results are compared with the results of the previous literature to validate the performance of the proposed technique. The results prove that FPA achieves the least error between the extracted and the measured data relative to the other techniques over the entire ranges of different environmental conditions, specially at low irradiation levels. Moreover, FPA outperforms the other techniques from the point of view of both the convergence speed and the convergence time. In addition, comparison of (I–V) characteristics of the extracted parameters by FPA and that of the experimental data shows unnoticed deviation between them. That is why the Flower Pollination Algorithm is recommended as the fastest and the most accurate optimization technique for the optimal parameters extraction process.