Parameter Extraction Algorithm Research Articles

Analytical Uncertainty Evaluation of Material Parameter Measurements at THz Frequencies

Material parameter extraction algorithms are studied and simplified both for transmission-reflection and transmission-only methods. The simplified relations, which are closed-form in some cases, are analyzed to establish the uncertainty sensitivity coefficients and therefore, to clarify the main uncertainty contributions and reduce the systematic and random errors. Simple closed-form expressions presented in this paper show the sensitivity of the extracted permittivity to each input parameter such as S21 (phase and amplitude), frequency, and the material thickness. Results are presented for several material slabs for three waveguide frequency ranges 75–110 GHz, 140–220 GHz, and 500–750 GHz using VNA-based free-space technique in the THz domain. Comparison of results (and the associated uncertainties) between different algorithms can help to choose the optimal one suitable for lossy or low-loss materials, and thin or thicker slabs. This can explain why the same set of S-parameters data usually gives different final results (permittivity and permeability) with different algorithms and verify the reliability of the calibration and extraction process.

A per-unit-single-diode-model parameter extraction algorithm: A high-quality solution without reduced-dimensions search

Recently, the photovoltaic (PV) parameter extraction using the fitting process between the measured current–voltage (I-V) curve and the single-diode-model (SDM) is recognised as a non-convex optimization problem with multiple local minima. Due to this nature, the direct use of the deterministic methods is not found suitable for the global optimum solution. However, the global optimum solution is only possible when the SDM is used with reduced-dimensions search. Standing opposite to this concept, the present work claims to have global optimum solution without reduced-dimensions search in the SDM. In order to prove this claim, a per-unit-single-diode-model parameter extraction algorithm (PUSDM-PEA) is developed which establishes a nonlinear least-squared-fitting between the measured I-V curve and the PUSDM. Through multiple tests on various cells and modules, commonly employed in the PV literature, the findings show that the convergence to the global optimum is always guaranteed if initial ideality factor n є [0.5, 2.5]. In addition, the computation cost is highly influenced by the initial n selection, and there exists a single or a couple of initial n values with the minimum computation cost. Only I-V curve as an input without any additional PV module information, the high-quality PV solution is obtained for the full and partial I-V curves with the minimum computation cost.

Terahertz Dielectric Spectroscopy Based Thermal Aging Analysis of Polypropylene

Polypropylene (PP) is a widely used common type of polymer with excellent chemical and mechanical properties. Thermal aging is a main culprit in degrading its lifetime and effectiveness. In this article, terahertz (THz) dielectric frequency response of PP during the process of thermal aging is monitored and analyzed to understand the underlying mechanism and to qualify THz dielectric spectroscopy as a useful tool for time-stamping and benchmarking the thermal aging of polymer materials. Using an improved THz material parameter extraction algorithm for thin films, the real and imaginary parts of the dielectric permittivity of standard PP test coupons were obtained at various stages of the thermal aging process. In agreement with theoretical analysis based on a molecular-level understanding of the thermal aging dynamics, the experimental results indicated THz wave transmission loss of PP correlated quantitatively with the degree of its thermal aging.

Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models

Internal resistance is one of the important parameters in the Li-Ion battery. This paper identifies it using two different methods: electrochemical impedance spectroscopy (EIS) and parameter estimation based on equivalent circuit model (ECM). Comparing internal resistance, the conventional parameter estimation method yields a different value than EIS. Therefore, a hysteresis-free parameter identification method based on ECM is proposed. The proposed technique separates hysteresis resistance from the effective resistance. It precisely estimated actual internal resistance, which matches the internal resistance obtained from EIS. In addition, state of charge, open circuit voltage, and different internal equivalent circuit components were identified. The least square method was used to identify the parameters based on ECM. A parameter extraction algorithm to interpret impedance spectrum obtained from the EIS. The algorithm is based on the properties of Nyquist plot, phasor algebra, and resonances. Experiments were conducted using a cellphone pouch battery and a cylindrical 18650 battery.

Parameterised Resource-Bounded ATL

It is often advantageous to be able to extract resource requirements in resource logics of strategic ability, rather than to verify whether a fixed resource requirement is sufficient for achieving a goal. We study Parameterised Resource-Bounded Alternating Time Temporal Logic where parameter extraction is possible. We give a parameter extraction algorithm and prove that the model-checking problem is 2EXPTIME-complete.

An Adaptive Wind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model

This paper presents a new methodology to extract the unknown parameters of a single-diode photovoltaic (PV) cell model. The first contribution of this paper is the development and implementation of a new version of the wind-driven optimization algorithm, called an adaptive wind-driven optimization (AWDO) algorithm. The advantages of the AWDO algorithm are: 1) accurate extraction of the global values of the optimized PV parameters in changing weather conditions, which is achieved by building solutions from random operations; and 2) capability of handling the given complex multi-modal and multi-dimensional optimization problems. The second contribution is the identification of a generalization model to generalize the extracted parameters of a single-diode PV cell model. That provides an ability of the proposed methodology to work with any I–V characteristic curve of PV cells and at any weather condition on a 15-min basis. To validate the proposed methodology, it has been tested for 1307 I–V characteristic curves of a PV module at various weather conditions on a 15-min basis. Additionally, its accuracy and computational efficiency are verified and compared with five well-known existing extraction methods: Villalva's model, particle swarm optimization, biogeography-based optimization, Gang's model, and bacterial foraging optimization by both simulation and outdoor measurements. The results show that the AWDO algorithm can provide the extracted five parameters with an acceptable range of accuracy and faster than the aforementioned models. Therefore, the proposed methodology (AWDO based on Chenlo's model) can be confidently recommended as a reliable, feasible, valuable, and fast optimization algorithm for parameter extraction of a single-diode PV cell model.

Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm

While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.

An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models

Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models.

Tree Growth Based Optimization Algorithm for Parameter Extraction of Different Models of Photovoltaic Cells and Modules

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer’s PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters’ identification of PV of solar cells.

Bayesian inversion for a biofilm model including quorum sensing

We propose a mathematical model based on a system of partial differential equations (PDEs) for biofilms. This model describes the time evolution of growth and degradation of biofilms which depend on environmental factors. The proposed model also includes quorum sensing (QS) and describes the cooperation among bacteria when they need to resist against external factors such as antibiotics. The applications include biofilms on teeth and medical implants, in drinking water, cooling water towers, food processing, oil recovery, paper manufacturing, and on ship hulls. We state existence and uniqueness of solutions of the proposed model and implement the mathematical model to discuss numerical simulations of biofilm growth and cooperation. We also determine the unknown parameters of the presented biofilm model by solving the corresponding inverse problem. To this end, we propose Bayesian inversion techniques and the delayed-rejection adaptive-Metropolis (DRAM) algorithm for the simultaneous extraction of multiple parameters from the measurements. These quantities cannot be determined directly from the experiments or from the computational model. Furthermore, we evaluate the presented model by comparing the simulations using the estimated parameter values with the measurement data. The results illustrate a very good agreement between the simulations and the measurements.

Modal parameter extraction from measured signal by frequency domain decomposition (FDD) technique

Purpose The purpose of this paper is to highlight that the need for improved system identification methods within the domain of modal analysis increases under the impulse of the broadening field of applications, e.g., damage detection and vibro-acoustics, and the increased complexity of today’s structures. Although significant research efforts during the last two decades have resulted in an extensive number of parametric identification algorithms, most of them are certainly not directly applicable for modal parameter extraction. So, based on this, the aim of the present work is to develop a technique for modal parameter extraction from the measured signal. Design/methodology/approach A survey and classification of the different modal analysis methods are made; however, the focus of this thesis is placed on modal parameter extraction from measured time signal. Some of the methods are examined in detail, including both single-degree-of-freedom and multi-degree-of-freedom approaches using single and global frequency-response analysis concepts. The theory behind each of these various analysis methods is presented in depth, together with the development of computer programs, theoretical and experimental examples and discussion, in order to evaluate the capabilities of those methods. The problem of identifying properties of structures that possess close modes is treated in particular detail, as this is a difficult situation to handle and yet a very common one in many structures. It is essential to obtain a good model for the behavior of the structure in order to pursue various applications of experimental modal analysis (EMA), namely: updating of finite element models, structural modification, subsystem-coupling and calculation of real modes from complex modes, to name a few. This last topic is particularly important for the validation of finite element models, and for this reason, a number of different methods to calculate real modes from complex modes are presented and discussed in this paper. Findings In this paper, Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been developed based on frequency domain decomposition (FDD) technique to check the accuracy of the results as obtained from ARTeMiS. It is observed that the frequency domain-based algorithm shows good agreement with the extracted results. Hence the following conclusion may be drawn: among several frequency domain-based algorithms for modal parameter extraction, the FDD technique is more reliable and it shows a very good agreement with the experimental results. Research limitations/implications In the case of extraction techniques using measured data in the frequency domain, it is reported that the model using derivatives of modal parameters performed better in many situations. Lack of accurate and repeatable dynamic response measurements on complex structures in a real-life situation is a challenging problem to analyze exact modal parameters. Practical implications During the last two decades, there has been a growing interest in the domain of modal analysis. Evolved from a simple technique for troubleshooting, modal analysis has become an established technique to analyze the dynamical behavior of complex mechanical structures. Important examples are found in the automotive (cars, trucks, motorcycles), railway, maritime, aerospace (aircrafts, satellites, space shuttle), civil (bridges, buildings, offshore platforms) and heavy equipment industry. Social implications Presently structural health monitoring has become a significantly important issue in the area of structural engineering particularly in the context of safety and future usefulness of a structure. A lot of research is being carried out in this area incorporating the modern sophisticated instrumentations and efficient numerical techniques. The dynamic approach is mostly employed to detect structural damage, due to its inherent advantage of having global and location-independent responses. EMA has been attempted by many researchers in a controlled laboratory environment. However, measuring input excitation force(s) seems to be very expensive and difficult for the health assessment of an existing real-life structure. So Ambient Vibration Analysis is a good alternative to overcome those difficulties associated with the measurement of input excitation force. Originality/value Three single bay two storey frame structure has been chosen for the experiment. The frame has been divided into six small elements. An algorithm has been developed to determine the natural frequency of those frame structures of which one is undamaged and the rest two damages in single element and double element, respectively. The experimental results from ARTeMIS and from developed algorithm have been compared to verify the effectiveness of the developed algorithm. Modal parameters like mode shapes and natural frequencies are extracted using an FFT analyzer and with the help of ARTeMiS, and subsequently, an algorithm has been programmed in MATLAB based on the FDD technique to check the accuracy of the results as obtained from ARTeMiS. Using singular value decomposition, the power Spectral density function matrix is decomposed using the MATLAB program. It is observed that the frequency domain-based algorithm shows good consistency with the extracted results.

A Pole-Extraction Algorithm for Wall Characterization in Through-the-Wall Imaging Systems

Estimation of wall parameters is critical for focused through-the-wall imaging (TWI). In this paper, a novel algorithm based on pole extraction and wall reflection coefficients is presented to accurately estimate the essential parameters of a multi-layered wall. The generalized pencil of function (GPOF) is applied to decompose the frequency-domain received signal into a summation of ordered exponential terms and phase delays. Matching the terms with the multiple wall reflections provides equations for extracting the parameters. A frequency-domain calibration process is proposed to remove the antenna response from the measured wall response. The formulation of the proposed parameter extraction and calibration algorithms is presented along with the simulated and measured results for validation. The efficacy of the method is demonstrated on a real TWI system.

Compact and high-precision wavemeters using the Talbot effect and signal processing.

Precise knowledge of a laser's wavelength is crucial for applications from spectroscopy to telecommunications. Here, we present a wavemeter that operates on the Talbot effect. Tone parameter extraction algorithms are used to retrieve the frequency of the periodic signal obtained in the sampled Talbot interferogram. Theoretical performance analysis based on the Cramér-Rao lower bound as well as experimental results are presented and discussed. With this scheme, we experimentally demonstrate a compact and high-precision wavemeter with below 10pm single-shot estimation uncertainty under the 3-σ criterion around 780nm.

A fast key parameter extraction algorithm for long fiber distributed sensing based on Brillouin scattering.

A fast key parameter extraction algorithm is proposed to improve the real-time performance of temperature and strain measurements when performing Brillouin scattering-based fiber-distributed sensing. The algorithm uses a new initial value method that takes the extracted key parameters of the current point in the fiber as the initial guesses for the next point. Based on the old and new initial value method, the existing objective method, optimization algorithm, and convergence criterion, the key parameter extraction algorithms developed are implemented in Matlab using the typical Lorentzian, Gaussian, and pseudo-Voigt profiles. These algorithms are used to extract the parameters over a large range of measured Brillouin spectra for the entire fiber with different averaging times. The results reveal that apart from the case when the frequency sweep spans is less than the linewidth and the pseudo-Voigt profile is used (in this case, the mean computation time of the proposed algorithm is 1.1% larger than that of the referenced algorithm), the proposed algorithm not only ensures high accuracy in extracting the key parameters, but also improves the arithmetic efficiency by 16.3%-49.1%.

A parameter extraction algorithm for microwave filter based on adaptive interpolation fitting

This article proposes a parameter extraction algorithm based on adaptive interpolation fitting. By adaptively inserting interpolation points within a given interval, the number of the sampling points in the extraction process can be greatly reduced, which makes the calculation more efficient. Furthermore, the effect of noise can be eliminated with the multipath characteristic of the algorithm. The specific algorithm flow is given in detail. And two microwave filter design examples verified the feasibility and effectiveness of the proposed algorithm.

Feature extraction of precession target based on Doppler profile sequences by maximum likelihood estimation

The micro-motion feature extraction of precession target is an effective way for discriminating warhead from decoy in missile ballistic defence. Generally, traditional methods based on high-resolution range profile (HRRP) sequences put forward high requirements on modulation bandwidth for the system, increasing the complexity of the system significantly. Utilising single-frequency ladar without modulation, the authors proposed a precession parameter extraction algorithm of precession target based on high-resolution Doppler profile (HRDP) sequences. Firstly, they model the Doppler bandwidth of the cone-shaped precession target with the observation angle. Under the assuming that the measurement error obeys zero-mean Gaussian distribution, the probability density function of Doppler bandwidth is established. Then, the precession parameters are estimated with measured Doppler profile sequences through maximum likelihood estimation method. Compared with the methods based on HRRPs, the proposed method based on the Doppler profile has lower requirements on the system and easier to implement. Laboratorial data has been used to validate the feasibility of the proposed method.

An accurate and simplified small signal parameter extraction method for GaN HEMT

SummaryIn this paper, development of a small signal model for 2 × 200 μm GaN HEMT based on the conventional 20‐element model is presented. The proposed model presents a direct parameter extraction algorithm, instead of the hybrid optimization approach, that provides simplification, accuracy, and less computational complexity. The extrinsic elements are extracted using a modified cold pinch‐off condition while discarding the unwanted forward biasing of the gate. The negative drain to source capacitance Cds is also observed in the ohmic region (for smaller VDS). An excellent agreement found between the measured and modeled data for a wide range of frequencies and bias values shows the effectiveness of the proposed approach. The proposed modeling technique is validated with a good agreement between the achieved bias dependency of intrinsic parameter values and the respective theoretical parameter values.

Statistical MOSFET Modeling Methodology for Cryogenic Conditions

Conventional transistor models are unable to capture the electrical behavior of transistors at cryogenic temperatures. In this paper, a methodology has been developed to calibrate temperature dependence parameters of Berkeley Short-Channel Insulated Gate Field Effect Transistor Model (BSIM3). Rather than modifying BSIM3 equations, the algorithm only changes the values of relevant parameters through noniterative, analytic operations; therefore, it can be implemented in typical circuit simulation tools. Proposed methodology allows to estimate ${I}_{D}$ – ${V}_{\textsf {GS}}$ and ${I}_{D}$ – ${V}_{\textsf {DS}}$ curves of the transistors having different channel lengths and widths even under various operating voltages, including the body bias. The parameter extraction algorithm runs with a reasonable computation cost and can compute parameter sets for transistors at user-defined cryogenic conditions.

Research on feature extraction algorithm for plantar pressure image and gait analysis in stroke patients

The plantar pressure image is an important tool for gait analysis. It has important applications in evaluating the recovery of stroke patients after operation and formulating the rehabilitation training program. It is one of the key technologies of gait analysis to extract foot feature parameters from static/dynamic plantar pressure images. This article deals with the noise in the original image through the piecewise linear grayscale transformation, the time domain mean filter and the maximum value filter, then determine the position of the feet in the image by the foot localization algorithm based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and the K-means clustering method. Finally, the plantar pressure feature parameters were extracted according to the positioned images. Based on the above feature parameter extraction algorithm, the plantar pressure feature parameters of 20 healthy subjects and 20 S patients with relative recovery period (2–6 months after the onset) were compared, showing a statistically significant difference (P < 0.001). Based on the above data, gait characteristics of stroke patients were further analyzed.

An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models

Solar Photovoltaic systems (SPVSs) are becoming one of the most popular renewable energy technology for generating significant share of electric power. With the consistent growth of SPVSs applications, the challenge of parameters estimation of photovoltaic cells has drawn the attention of researchers and industrialists and gained immense momentum for SPVSs modeling. This paper proposes an efficient approach based on Salp Swarm Algorithm (SSA) for extracting the parameters of the electrical equivalent circuit of PV cell based double-diode model. The experimental and comparative results demonstrate that SSA is highly competitive with the results of two algorithms that have never been used before for the PV cell parameter extraction namely Sine Cosine Algorithm (SCA) and Virus Colony Search Algorithm (VCS). SSA is also significantly better than three well-established parameter extraction algorithms namely Ant Lion Optimizer (ALO), Gravitational Search Algorithm (GSA) and Whale Optimization Algorithm (WOA). Several evaluation criteria including Mean Square Error (MSE), Absolute Error (AE) and statistical criterion show that the SSA algorithm provides the highest value of accuracy and has merits in designing SPVSs.