Parameter Identification Research Articles

Modeling the Efficiency of Biogas Plants by Using an Interval Data Analysis Method

This article considers the task of developing mathematical models and their computer implementation that would establish the dependence of pH (acidity of the environment) on the volume and structure of raw materials for daily loading, as well as on the operating parameters of temperature and humidity based on the interval analysis of experimental data obtained during BGP research of a given type. In the process of research, based on the developed interval models, it was established that this indicator depends on the volume and structure of raw materials, as well as on the temperature and humidity of the substrate in the bioreactor. To build this mathematical model, it is proposed to use the method of interval data analysis and the method of identification of model parameters based on multidimensional optimization. The results of experimental studies for a specific type of biogas plant are given, and interval models with guaranteed prognostic properties that characterize the pH of the environment depending on the specific type of bio-raw material of solid and liquid fractions, temperature, and humidity are obtained. Based on the use of different types of raw materials, the developed models, based on experimental data, describe different configurations of the structure and volumes of raw materials for daily loading. The obtained mathematical models are an algebraic nonlinear equation that can be applied to control the level of pH of the environment in the bioreactor by determining the optimal volumes of raw materials of each type during the loading period depending on the temperature and humidity of the substrate in the bioreactor.

Delving into the Complexity of Analogical Reasoning: A Detailed Exploration with the Generalized Multicomponent Latent Trait Model for Diagnosis.

Research on analogical reasoning has facilitated the understanding of response processes such as pattern identification and creative problem solving, emerging as an intelligence predictor. While analogical tests traditionally combine various composition rules for item generation, current statistical models like the Logistic Latent Trait Model (LLTM) and Embretson's Multicomponent Latent Trait Model for Diagnosis (MLTM-D) face limitations in handling the inherent complexity of these processes, resulting in suboptimal model fit and interpretation. The primary aim of this research was to extend Embretson's MLTM-D to encompass complex multidimensional models that allow the estimation of item parameters. Concretely, we developed a three-parameter (3PL) version of the MLTM-D that provides more informative interpretations of participant response processes. We developed the Generalized Multicomponent Latent Trait Model for Diagnosis (GMLTM-D), which is a statistical model that extends Embretson's multicomponent model to explore complex analogical theories. The GMLTM-D was compared with LLTM and MLTM-D using data from a previous study of a figural analogical reasoning test composed of 27 items based on five composition rules: figure rotation, trapezoidal rotation, reflection, segment subtraction, and point movement. Additionally, we provide an R package (GMLTM) for conducting Bayesian estimation of the models mentioned. The GMLTM-D more accurately replicated the observed data compared to the Bayesian versions of LLTM and MLTM-D, demonstrating a better model fit and superior predictive accuracy. Therefore, the GMLTM-D is a reliable model for analyzing analogical reasoning data and calibrating intelligence tests. The GMLTM-D embraces the complexity of real data and enhances the understanding of examinees' response processes.

Dynamic regressor extension and mixing control Lyapunov functions and high-order control Barrier functions

This paper studies the safety-critical tracking control problem for unknown structured systems with bounded disturbances and high relative degree. Firstly, a dynamic regressor extension and mixing high-order control barrier function (DREM-HOCBF) is constructed with a DREM-based robust finite-time parameter identification law. Our approach directly enforces forward invariance of the system containing parameter estimates and worst-case estimation errors to ensure system safety against parameterised uncertainty. Due to DREM, this method can approach the safety set boundary and reduce the conservatism. Meanwhile, it's also applicable to higher relative degree constraints that are more common in actual systems. Then, a DREM control Lyapunov function (DREM-CLF) that leverages the same worst-case estimation errors is proposed to guarantee that the tracking errors converge exponentially. Moreover, the method's robustness against bounded disturbances is considered. Finally, the feasibility of the developed method is verified via an adaptive cruise control (ACC) problem with unknown parameters and bounded disturbances.

Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

Hydrodynamic Parameter Identification of Deep‐Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter

A Systematic Review and Bayesian Meta-Analysis of Acoustic Measures of Prosody in Parkinson's Disease.

Linguistic prosody is affected in Parkinson's disease (PD), which implicates the basal ganglia's role in the production of prosody. However, there is no recent systematic synthesis of the available acoustic evidence of prosodic impairment in PD. This study aimed to identify the acoustic features of linguistic prosody that are consistently affected in PD. The authors systematically reviewed articles that reported acoustic features of prosodic production in PD. Articles focused on fundamental frequency (F0) and its variability, intensity and its variability, speech and articulation rate, and pause duration and ratio. From a total of 648 records identified, 36 met criteria for inclusion and exclusion. For each acoustic measurement and task, data from people with PD (PwPD) were compared with those from controls to extract effect sizes. Pooled effect sizes were estimated using robust Bayesian hierarchical regression models. PD was associated with decreased F0 variability and increased pause duration. There was limited evidence of reduced intensity variability and speech rate in PwPD. No evidence was found to suggest that PD affects articulation rate or pause ratio. The primary acoustic parameters of prosody affected by PD are F0 variability and pause duration. The identification of these acoustic parameters has important clinical implications for the selection of PD management strategies. The association of F0 variability and pause duration with PD suggests that the neural circuits controlling these parameters are at least partly shared and might include the basal ganglia. While the current study focused on the phonetic realization of prosodic cues, future studies should examine whether and how PD affects prosody at higher levels of processing. https://doi.org/10.23641/asha.25892923.

Experimental parameter identification of scalable white and grey box jounce bumper models for ride comfort simulation

The design of a passenger car’s ride and handling goals requires an enormous amount of hardware and prototypes. To avoid time-consuming testing activities, one crucial trend is the virtualisation of suspension development. Another goal of the virtualisation is the determination of vehicle properties at an early stage of the development process. Highly accurate suspension models are required to transfer the full vehicle’s characteristics into simulation tools. Since the jounce bumper is an important component of the suspension system, modelling its functional behaviour is essential for virtualised chassis development. Therefore, two dynamic modelling approaches are derived and compared in this contribution. One approach is based on local linear model networks and thus can be seen as a grey box model. The other approach consists of white box rheological elements, which allow the physical interpretability of the model parameters. The comparison of the approaches is based on experimental parameter identification, which includes synthetic and real excitations recorded on representative roads. The advantages of the proposed approaches will be discussed in terms of their suitability for the virtual design of the driving behaviour.

The Value Logic of Digital Technology Empowering the Deep Integration of Culture-tourism in Huaian Section of Grand Canal

In response to the problems of weak identification of cultural and tourism products, insufficient driving force of the cultural and tourism industry to economic development, and low level of dissemination and innovation of culture in Huaian section of Grand Canal, it is proposed that value innovation is the internal logical path to promote cultural and tourism integration and achieve sustainable development of products, industries, and culture of cultural and tourism; The empower- ment of digital technology is an external logical path to promote the deep integration of cultural and tourism and achieve iteration and spiral development of products, industries, and cultural of cultural and tourism. It is revealed that the value logic of the deep integration of cultural and tourism and is used to guide the development of products and industries , the culture utilization of cultural and tourism in the Huaian section of Grand Canal. The aim is to make the cultural-tourism products fresh, the cultural-tourism industry revitalized, and the cultural-tourism spirit survival long-term.

Optimization of Sensor Placement for a Measurement System for the Determination of Local Magnetic Material Properties

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

Forward and inverse problems for creep models in viscoelasticity.

This study examines a class of time-dependent constitutive equations used to describe viscoelastic materials under creep in solid mechanics. In nonlinear elasticity, the strain response to the applied stress is expressed via an implicit graph allowing multi-valued functions. For coercive and maximal monotone graphs, the existence of a solution to the quasi-static viscoelastic problem is proven by applying the Browder-Minty fixed point theorem. Moreover, for quasi-linear viscoelastic problems, the solution is constructed as a semi-analytic formula. The inverse viscoelastic problem is represented by identification of a design variable from non-smooth measurements. A non-empty set of optimal variables is obtained based on the compactness argument by applying Tikhonov regularization in the space of bounded measures and deformations. Furthermore, an illustrative example is given for the inverse problem of isotropic kernel identification. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

Stochastic force identification for uncertain structures based on matrix equilibration and improved Tikhonov regularization method

Accurate identification and estimation of stochastic forces applied to in-service engineering structures play a vital role in structural safety assessments. This study devised an effective force power spectral density (PSD) identification method to address the challenge of identifying multipoint stationary stochastic forces in uncertain structures. Initially, a probability model was employed to characterize structural uncertainties. Subsequently, an integral relationship was established between the probability density function (PDF) of the random structural parameters and that of the stochastic force PSD. By employing a point-selection technique based on the generalized F-discrepancy and a smoothing method, the uncertainty problem was transformed into a finite number of stochastic force PSD identification problems for deterministic structures. Simultaneously, based on the inverse pseudo-excitation method, a matrix equilibration approach and an improved Tikhonov regularization method were used to address the problem of large identification errors near structural natural frequencies. In comparison to the traditional weighting matrix method, the proposed method further reduces the condition number of frequency response function matrices, thereby enhancing the accuracy of force PSD identification. Finally, numerical examples were presented to validate the effectiveness of the proposed method in solving the stochastic force identification problem.

Modeling the Performance Parameters of Pollen Grains of Male Date Palms Using an Artificial Neural Network Based on the Mineral Composition and Morphological Properties of Their Leaves

One of the key factors for sustainability in agricultural systems, particularly, for cultivation of date palms, is the identification of the performance parameters of the pollen grains of male date palms (Phoenix dactylifera L.). This study was carried out to predict the performance parameters of pollen grains using an artificial neural network (ANN) model. The morphological features of spathe length, spathe weight, number of pinnae per leaf, leaf length, leaf width, length of the pinna part, pinna length, pinna width, length of the spathe stem, and spathe width, as well as the concentrations of minerals such as Mg, N, K, P, and Ca in their leaves were used as inputs to the ANN model. For this purpose, we collected the required data from nine male date palms grown in Saudi Arabia. The ANN model utilized in this work included an input layer with 15 parameters, a hidden layer of 30 neurons, and an output layer with 8 neurons. The ANN model was trained with 27 patterns. Seven patterns were utilized for testing purposes. The coefficient of determination (R2) obtained between the observed and predicted performance parameters’ values using the testing dataset was 0.902 for the number of strands per spathe, 0.967 for strand length, 0.963 for the number of flowers per strand, 0.941 for the number of flowers per spathe, 0.985 for the weight of pollen grains per spathe, 0.810 for the pollen grains’ viability, 0.936 for the pollen grains’ length, and 0.992 for the pollen grains’ width. The length of the spathe stem had the most critical effect on how the ANN model predicted the values of the dependent variables, i.e., the number of strands per spathe, with a percentage of contribution of 17.66%; the weight of pollen grains per spathe, with 17.85%; the pollen grains’ length, with 19.78%, and the pollen grains’ width, with a percentage of contribution of 30.59%. Spathe weight had the most critical influence on strand length and pollen grains’ viability, with percentages of 26.29% and 14.92%, respectively. Leaf width had the most critical effect on the number of flowers per spathe, with a percentage of 12.55%. The elemental concentration of K in the male date palm leaves had the most critical effect on the number of flowers per strand, with a percentage of 13.98%. It was therefore concluded that using a modeling process with the ANN technique can help estimate the performance parameters of male date palms’ pollen grains for different purposes, such as providing a starting point for mathematical analyses associated with the physiological mechanisms of male date palm. Moreover, the outcomes of this research work can be supportive as a practical tool in this field of study.

Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification

The open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained by using load–displacement curves derived from simulations of a boundary value problem—instead of using data generated for homogeneous states of deformation at material point or one-element level—with varying material parameter combinations. The ANN is then employed so as to predict sets of material parameters that already provide close solutions to the experiment. These predicted parameter sets serve as starting values for a subsequent multi-objective parameter identification by using ADAPT. ADAPT allows for the consideration of input data from multiple scales, including integral data such as load–displacement curves, full-field data such as displacement and strain fields, and high-resolution experimental void data at the micro-scale. The influence of each data set on prediction quality is analyzed. Using various types of input data introduces additional information, enhancing prediction accuracy. The validation is carried out with respect to experimental void measurements of forward rod extruded parts. The results demonstrate, by incorporating void measurements in the optimization process, that it is possible to improve the quantitative prediction of ductile damage in the sense of void area fractions by factor 28 in forward rod extrusion.

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Multi-lane vehicle load measurement using bending and shear strains

Abstract Many load identification methods have been proposed, but most are affected by the basic axle parameters and lateral distribution of vehicles. To effectively measure traffic flow with lateral distribution information, this article presents an innovative method that employs a strain decoupling model (SDM) and a vehicle information identification model (VIDM) to measure multi-lane vehicle load depending on the bending strain and shear strain from long-gauge fiber bragg grating (FBG) sensors. The SDM decouples the measured coupling strain into the strain for a single lane load, thereby simplifying the complex structural response resulting from lateral distributed vehicles. By exploiting the distinct characteristics of different strain types that reflect various aspects of the structure, the VIDM establishes a sophisticated mapping relationship between bending, shear strain and axle parameters, which enables the accurate determination of axle parameters including axle speed and spacing. The real-time estimation of the multi-lane vehicle load is achieved by combining the obtained axle information with the decoupled bending strain. This method effectively solves the problem of large load estimation error caused by inaccurate identification of axle parameters, and enables accurate acquisition of vehicle load in lateral distribution using bending and shear strains near the bridge entrance. Both numerical studies and laboratory tests are carried out on a simply supported beam for conceptual verification. The results demonstrate that the proposed method successfully improves the measurement of multi-lane vehicle load.

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error

AbstractSensorless control of permanent magnet synchronous motor (PMSM) can increase the reliability of electric actuators of more electrical aircraft. Numerical online parameter estimation method will enhance the performance for sensorless control methods based on sliding mode observer (SMO). However, numerical online parameter identification methods typically assume zero rotor position estimation error to ensure full rank of the identification equation set. This assumption actually leads to errors in parameter identification, resulting in steady‐state errors in rotor position estimation. This paper focuses on a compensation method for PMSM online parameter identification that takes into account rotor position estimation error. This method is used for sensorless control based on SMO, using model reference adaptive systems for online parameter identification, and no additional parameter adjustment required. Validation is conducted on a PMSM experimental platform with maximum load of 15kW. The experimental results of 1000 r/min steady state operation proved the effectiveness of the method, and the dynamic experiments with 5 and 15kW loads proved that the method has no significant adverse effect on the system dynamics.

An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification

Aiming at the high dimension and complexity of parameters identification problem, a whale optimization algorithm based on event-triggered and dimension learning scheme (EDWOA) is presented, which is specifically designed for the proton exchange membrane fuel cell (PEMFC) model. Drawing inspiration from group optimization strategies, a novel dimensional learning method is introduced to enhance the dynamic search capabilities of the algorithm. To assess the efficacy of the proposed algorithm, benchmark function testing was conducted, and its fitness surpassed common heuristic algorithms on over 15 objective functions. The results clearly indicate that the EDWOA algorithm outperforms its counterparts in terms of global search performance. Its ability to navigate complex search spaces sets it apart from other algorithms. Finally, the proposed EDWOA algorithm is successfully applied to parameter identification in the PEMFC model. Through a comparative analysis with existing research findings, it was found that the identified PEMFC model exhibited a notable enhancement in fitness, ranging from 0.02 to 0.93. This underscores the effectiveness of the EDWOA algorithm in improving the performance and dynamic output of PEMFC models.

Bayesian Parameter Identification in Impedance Boundary Conditions for Helmholtz Problems

Bayesian Parameter Identification in Impedance Boundary Conditions for Helmholtz Problems

Semi-empirical model of the twin-screw refrigeration compressor with capacity control devices

Adopting twin-screw refrigeration compressors with capacity control devices has become the dominant trend to meet varying cooling demands. To analyze the compressor performance with different capacity control methods and further explore the potential, a semi-empirical model with 16 characteristic parameters is proposed. Detailed working processes under the part-load condition (PC) are considered and the corresponding calculations, including pressure loss, power loss, and leakage are innovatively refined. Subsequently, a prototype experiment was carried out for parameters identification and model validation. A good agreement between the experimental and simulated values is attained, with average relative errors of 3.46 % and 1.15 % for shaft power and mass flow rate under the variable frequency condition (VFC) and 3.18 % and 4.63 % under the PC. Thermodynamic simulation results indicate that both shaft power and mass flow rate are linearly correlated with frequency and the efficiencies are nearly stable over the whole interval. However, as the load decreases, the performance decays significantly and the most remarkable change occurs at a position of between 0.9 and 1. The volumetric and adiabatic efficiencies of the VFC are 3.65 and 2.26 times higher than that of the PC when the mass flow rate reaches the lower limit.