Parameter Identification Research Articles

MODELS OF REMOTE IDENTIFICATION OF PARAMETERS OF DYNAMIC OBJECTS USING DETECTION TRANSFORMERS AND OPTICAL FLOW

The tasks of remote identification of parameters of dynamic objects are important for various fields, including computer vision, robotics, autonomous vehicles, video surveillance systems, and many others. Traditional methods of solving these problems face the problems of insufficient accuracy and efficiency of determining dynamic parameters in conditions of rapidly changing environments and complex dynamic scenarios. Modern methods of identifying parameters of dynamic objects using technologies of detection transformers and optical flow are considered. Transformer detection is one of the newest approaches in computer vision that uses transformer architecture for object detection tasks. This transformer integrates the object detection and boundary detection processes into a single end-to-end model, which greatly improves the accuracy and speed of processing. The use of transformers allows the model to effectively process information from the entire image at the same time, which contributes to better recognition of objects even in difficult conditions. Optical flow is a motion analysis method that determines the speed and direction of pixel movement between successive video frames. This method allows obtaining detailed information about the dynamics of the scene, which is critical for accurate tracking and identification of parameters of moving objects. The integration of detection transformers and optical flow is proposed to increase the accuracy of identification of parameters of dynamic objects. The combination of these two methods allows you to use the advantages of both approaches: high accuracy of object detection and detailed information about their movement. The conducted experiments show that the proposed model significantly outperforms traditional methods both in the accuracy of determining the parameters of objects and in the speed of data processing. The key results of the study indicate that the integration of detection transformers and optical flow provides reliable and fast determination of parameters of moving objects in real time, which can be applied in various practical scenarios. The conducted research also showed the potential for further improvement of data processing methods and their application in complex dynamic environments. The obtained results open new perspectives for the development of intelligent monitoring and control systems capable of adapting to rapidly changing environmental conditions, increasing the efficiency and safety of their work.

Design of chaotic circuit based on Knowm memristor

AbstractMost previous works in the field of nonlinear memristive chaotic systems mainly focus on non‐material memristor while seldom on material memristor. In this letter, the model and the corresponding parameter identification of the Knowm memristor is presented. The chaotic circuit based on the Knowm memristor is designed. Equilibrium points and stability of this Knowm‐memristor‐based chaotic system is analyzed. Nonlinear dynamic behavior is presented by the bifurcation diagram and Lyapunov exponents. Finally, the hardware circuit of this Knowm‐memristor‐based chaotic system is designed and the experimental results are presented for confirmation.

Learning the hard-to-learn: Active learning for imbalanced datasets in data-centric tunnel engineering

Data-driven methodologies hold the potential to revolutionize mechanized tunneling by informing decision-making. However, imbalanced data categories are prevalent within the realm of mechanized tunneling. Data-driven models often struggle to accurately predict data from minor sample categories, which, despite their scarcity, are crucial in practice. To address this bottleneck, this study introduces a committee-based active learning strategy to tackle the imbalanced sample identification problem. This strategy involves constructing multiple committee models to quantify the uncertainty in surrogate predictions. These specimens exhibiting high forecast uncertainty will be resampled with replacement from the validation data to iteratively augment the training data, thereby enhancing the model’s capability to handle imbalanced datasets. This study validates the strategy by applying it to two real tunneling engineering scenarios. The first case is to predict the grade of surrounding rock and the other one involves forecasting muck clogging in mechanised tunneling. The results demonstrate that the proposed strategy significantly improves the prediction accuracy of minority categories. Further, the study reveals that both the “entropy method” and “weight method” can effectively quantify the learning difficulty of samples. The same strategy can be equally applied to other fields of engineering and science.

A method for predicting needle insertion deflection in soft tissue based on cutting force identification

The deflection modeling during the insertion of bevel-tipped flexible needles into soft tissues is crucial for robot-assisted flexible needle insertion into specific target locations within the human body during percutaneous biopsy surgery. This paper proposes a mechanical model based on cutting force identification to predict the deflection of flexible needles in soft tissues. Unlike other models, this method does not require measuring Young’s modulus ( E ) and Poisson’s ratio ( ν ) of tissues, which require complex hardware to obtain. In the model, the needle puncture process is discretized into a series of uniform-depth puncture steps. The needle is simplified as a cantilever beam supported by a series of virtual springs, and the influence of tissue stiffness on needle deformation is represented by the spring stiffness coefficient of the virtual spring. By theoretical modeling and experimental parameter identification of cutting force, the spring stiffness coefficients are obtained, thereby modeling the deflection of the needle. To verify the accuracy of the proposed model, the predicted model results were compared with the deflection of the puncture experiment in polyvinyl alcohol (PVA) gel samples, and the average maximum error range predicted by the model was between 0.606 ± 0.167 mm and 1.005 ± 0.174 mm, which showed that the model can successfully predict the deflection of the needle. This work will contribute to the design of automatic control strategies for needles.

Improving Robotic Milling Performance through Active Damping of Low-Frequency Structural Modes

Industrial robots are increasingly prevalent due to their large workspace and cost-effectiveness. However, their limited static and dynamic stiffness can lead to issues like mode coupling chatter and regenerative chatter in robotic milling processes, even at shallow cutting depths. These problems significantly impact performance, product quality, tool longevity, and can damage robot components. An active inertial actuator was deployed at the milling spindle to enhance dynamic stiffness and suppress low-frequency vibrations effectively. It was identified that the characteristics of the actuator change with its mounting orientation, a common scenario in robotic machining processes. This variation has not been reported in the literature. Our study includes the identification of model parameters for the actuator in both horizontal and vertical mountings. Additionally, the novelty of the present work lies in the specific design and implementation of compensation filters tailored for the active inertial actuator in both horizontal and vertical configurations. These filters address the unique challenges posed by low-frequency vibrations in robotic milling, offering significant improvements in dynamic stiffness and vibration suppression. Traditional model-based compensators were effective for vertical mounting, while pole-zero placement techniques with minimum phase systems were optimal for horizontal mounting. These compensators significantly enhanced dynamic stiffness, reducing maximum low-frequency robot structural modes by approximately 100% in horizontal mounting and approximately 214% in the vertical configuration of the actuator. This advancement promises to enhance industrial robot capabilities across diverse machining applications.

Study of the microstructure of asphalt concrete using X-ray computed tomography

A mechanical digital twin (a mechanical finite-element model) of an asphalt concrete sample has been developed in the framework of a project for recycling polymer composite materials with fibrous reinforcement (fiberglass) as an alternative for crushed stone in the asphalt concrete production. A methodology of using X-ray computed tomography (XCT) for analysis of the asphalt concrete microstructure and calculation of the mechanical properties is developed. The data processing chain for developing a digital twin of the asphalt concrete microstructure, based on X-ray micro-computed tomography (XCT) image includes the following steps: 1) image enhancement; 2) image segmentation; 3) analysis of the morphology of pores and solid particles; 4) transformation of the segmented image into a voxels-based finite element (FE) model. It is demonstrated that the XCT resolution of 40 μm is sufficient for a reliable identification of microstructural parameters, i.e., volume fractions of the components, distributions of voids (pores) in size, shape and spatial position, as well as distributions of the crushed brittle additives (fiberglass chips) in size. The FE model constitutes a digital twin of the material, and, after specifying the characteristics of the material components, can be used for simulation of the thermomechanical and functional properties of the material. The developed procedure is exemplified in the calculation of statistics of the compression and shear moduli of the asphalt concrete with addition of crushed fiberglass particles. The dependence of the calculated elastic properties on the size of the digital twin is studied. It is shown that a model size of 10 mm and more is sufficient for the microstructural representativity and calculation of the homogenization characteristics. The results can be used for analysis of the microstructure and structure-dependent thermomechanical properties of asphalt concrete. The developed finite element model can be used for modelling of the visco-elastic response of asphalt concrete and its behavior under cyclic loading.

Shape and parameter identification by the linear sampling method for a restricted Fourier integral operator

In this paper we provide a new linear sampling method based on the same data but a different definition of the data operator for two inverse problems: the multi-frequency inverse source problem for a fixed observation direction and the Born inverse scattering problems. We show that the associated regularized linear sampling indicator converges to the average of the unknown in a small neighborhood as the regularization parameter approaches to zero. We develop both a shape identification theory and a parameter identification theory which are stimulated, analyzed, and implemented with the help of the prolate spheroidal wave functions and their generalizations. We further propose a prolate-based implementation of the linear sampling method and provide numerical experiments to demonstrate how this linear sampling method is capable of reconstructing both the shape and the parameter.

Identification and optimization of the key growth parameters involved in carotenoids production of the marine microalga Pavlova gyrans

In this work, a multivariate analysis was carried out, using a Plackett–Burman (PB) design involving seventeen growth parameters, on carotenoids production of Pavlova gyrans (p < 0.10). Each assay was analysed regarding its content (mg g−1) of fucoxanthin (Fx), diatoxanthin, diadinoxanthin, β-carotene (βCar), α-carotene, and the sum of all carotenoids analysed individually (TCar). According to the statistical analysis, modified medium formulations were developed for the particular cases of Fx, βCar, and TCar. The study showed that Fx content was positively affected by nitrogen supplementation and lower light intensities. Higher concentrations of nitrogen and iron increased the final content of βCar as well. Similarly, salinity, light intensity, nitrogen, iron, and cobalt were identified as key factors in TCar production. The PB-based formulations showed significant improvements (p < 0.05) for TCar (11.794 mg g−1) and Fx (6.153 mg g−1) when compared to the control conditions (Walne’s medium—2.010 mg g−1). Furthermore, effective control of key variables (e.g., light intensity) throughout P. gyrans growth proved successful (p < 0.05), increasing the productivity of Fx (0.759 mg L−1 d−1) and TCar (1.615 mg L−1 d−1).

A modified algebraic turbulent heat flux model for non-equilibrium and rotating flow and its application in film cooling simulation of a rotating turbine blade

Thermal fields and cooling flows over rotating turbine blades are usually a non-equilibrium anisotropic flow, and most turbulence models in RANS simulations do not provide acceptable accuracy. A modified model is proposed and assessed here. This is based on an explicit algebraic heat flux model that is a low-Reynolds-number version of the higher order generalized gradient diffusion hypothesis turbulent heat flux model. The presented model, called “Non-equilibrium and Sensible to Rotation Algebraic Heat Flux Model” or “NSR-AHFM” includes additional production terms added to the second moment closure of turbulent heat flux transport equation that considers system rotation and non-equilibrium effects in velocity field, while maintaining the frame invariance during transformation to a rotating coordinate system. The model was initially tested through a-priori analysis, comparing the model to corresponding quantities obtained from Direct Numerical Simulations, allowing for the identification of suitable model parameters. Subsequently, the proposed modified model was implemented in a CFD code to a representative configuration (i.e., rotating turbine blade) and results were compared against available experimental data from literature. The present model was undergone a comparative analysis with other similar models in both stationary and rotating channel flow scenarios. Also, the present model was applied to a film cooling simulation of a rotating turbine blade in different rotational speeds. The findings indicate that the present model exhibits superior performance when compared to other models. Moreover, its behavior aligns consistently with the underlying physics governing the flow dynamics of the rotating channel.

Using partial discharge data to identify highly sensitive electrochemical parameters of aged lithium-ion batteries

Highly sensitive (HS) electrochemical parameters can serve as battery aging indicators, deserving thorough examination. To identify these parameters and determine their cycle evolution across a wide range of battery state of health, this study conducts a three-stage analysis. First, after a detailed evaluation, a global sensitivity analysis is performed to evaluate the sensitivity values of ten selected parameters across five intervals of discharge voltage curves simulated through the Doyle-Fuller-Newman theory. Such analysis facilitates the classification of these parameters into HS and low sensitive (LS) groups. Next, a deep learning (DL) model is developed for parameter identification, employing as minimal data as possible, specifically only partial discharge curves and their corresponding first-order derivatives, as inputs, while the electrochemical parameters are generated as outputs. It is found that utilizing the model built with the HS parameters achieves a remarkably low mean absolute percentage error (MAPE) of 0.25 % for output prediction. On the contrary, adopting the LS parameters yields a MAPE of 13.30 %, highlighting the significant impact of parameter sensitivity on the model outputs. Lastly, the well-trained DL model is directly applied to identify the five HS parameters pertaining to real-world commercial batteries, whose state of health spans approximately from 97 % down to 55 %. With the experimentally recorded partial discharge voltage data, the model generates the cycle evolutions of the HS parameters, revealing a noticeable decrease in their values ranging from 12 % to 40 % with aging.

Material Parameter Identification for a Stress-State-Dependent Ductile Damage and Failure Model Applied to Clinch Joining

Similar to bulk metal forming, clinch joining is characterised by large plastic deformations and a variety of different 3D stress states, including severe compression. However, inherent to plastic forming is the nucleation and growth of defects, whose detrimental effects on the material behaviour can be described by continuum damage models and eventually lead to material failure. As the damage evolution strongly depends on the stress state, a stress-state-dependent model is utilised to correctly track the accumulation. To formulate and parameterise this model, besides classical experiments, so-called modified punch tests are also integrated herein to enhance the calibration of the failure model by capturing a larger range of stress states and metal-forming-specific loading conditions. Moreover, when highly ductile materials are considered, such as the dual-phase steel HCT590X and the aluminium alloy EN AW-6014 T4 investigated here, strong necking and localisation might occur prior to fracture. This can alter the stress state and affect the actual strain at failure. This influence is captured by coupling plasticity and damage to incorporate the damage-induced softening effect. Its relative importance is shown by conducting inverse parameter identifications to determine damage and failure parameters for both mentioned ductile metals based on up to 12 different experiments.

Two-dimensional oxygen-diffusion modelling for FLASH proton therapy with pencil beam scanning—Impact of diffusive tissue properties, dose, dose rate and scan patterns

Objective. Oxygen depletion is generally believed to play an important role in the FLASH effect—a differential reduction of the radiosensitivity of healthy tissues, relative to that of the tumour under ultra-high dose-rate (UHDR) irradiation conditions. In proton therapy (PT) with pencil-beam scanning (PBS), the deposition of dose, and, hence, the degree of (radiolytic) oxygen depletion varies both spatially and temporally. Therefore, the resulting oxygen concentration and the healthy-tissue sparing effect through radiation-induced hypoxia varies both spatially and temporally as well. Approach. We propose and numerically solve a physical oxygen diffusion model to study these effects and their dependence on tissue parameters and the scan pattern in pencil-beam delivery. Since current clinical FLASH PT (FLASH-PT) is based on 250 MeV shoot-through (transmission) beams, for which dose and dose rate (DR) hardly vary with depth compared to the variation transverse to the beam axis, we focus on the two-dimensional case. We numerically integrate the model to obtain the oxygen concentration in each voxel as a function of time and extract voxel-based and spatially and temporarily integrated metrics for oxygen (FLASH) enhanced dose. Furthermore, we evaluate the impact on oxygen enhancement of standard pencil-beam delivery patterns and patterns that were optimised on dose-rate. Our model can contribute to the identification of tissue properties and pencil-beam delivery parameters that are critical for FLASH-PT and it may be used for the optimisation of FLASH-PT treatment plans and their delivery. Main results. (i) the diffusive properties of oxygen are critical for the steady state concentration and therefore the FLASH effect, even more so in two dimensions when compared to one dimension. (ii) The FLASH effect through oxygen depletion depends primarily on dose and less on other parameters. (iii) At a fixed fraction dose there is a slight dependence on DR. (iv) Scan patterns optimised on DR slightly increase the oxygen induced FLASH effect. Significance. To our best knowledge, this is the first study assessing the impact of scan-pattern optimization (SPO) in FLASH-PT with PBS on a biological FLASH model. While the observed impact of SPO is relatively small, a larger effect is expected for larger target volumes. A better understanding of the FLASH effect and the role of oxygen (depletion) therein is essential for the further development of FLASH-PT with PBS, and SPO.

Identification and optimization of parameters for accurate quantification of RNA by RT-dPCR.

Reverse transcription-digital PCR (RT-dPCR) is attracting attention as a method that enables SI-traceable RNA quantification without calibration, but its accuracy and bias have not been thoroughly studied. In this study, the accurate quantification of RNA by the RT-dPCR method was investigated using NMIJ CRM 6204-b, an RNA certified reference material whose certified value was assigned by orthogonal chemical measurement methods. Moreover, a two-step RT-dPCR method was adopted to examine in detail the conditions for the RT reaction process, which was expected to be the major uncertainty component in the RT-dPCR measurement. Optimization experiments revealed that the type of reverse transcriptase, the concentration of template RNA, and the type and concentration of primers in the RT reaction affected the value quantifiedby RT-dPCR. Under the optimal conditions, the value quantifiedby RT-dPCR, 76.4ng/μL ± 6.7ng/μL (the quantified value ± expanded uncertainty (k = 2)), was consistent with the certified value, 68.2ng/μL ± 5.8ng/μL, of NMIJ CRM 6204-b RNA 1000-A within the expanded uncertainty. From the results of the uncertainty evaluation, the relative combined uncertainty of the RT-dPCR method was 4.42%, and the major uncertainty components in the RT-dPCR method were the preparation of RT solution (3.68%), the inter-day difference (1.80%), and the RT reaction (1.30%). Together, the results suggested that the contribution of the RT reaction process to the total uncertainty was greater than that of the dPCR process.

Identification of modal parameters of soil specimen based on impact force

Identification of modal parameters of soil specimen based on impact force

Practical prescribed‐time tracking control of unknown nonlinear systems: A low‐complexity approach

AbstractThis article is concerned with the trajectory tracking control problem for the nonlinear systems in the sense of the predefined settling time and accuracy. In contrast with the existing works, we focus on the cases where the system dynamics, its bounding functions, the unmatched disturbances, and the time‐varying parameters are totally unknown; the derivatives of the desired trajectory are not required to be available. They significantly challenge the identification and/or approximation‐based control solutions. To overcome this obstacle, a novel robust prescribed performance control approach via state feedback is put forward in this article. It not only ensures the natural satisfaction of the specific initial condition but also realizes a full‐time performance specification for trajectory tracking. Furthermore, for the case of unmeasured state variables, an output‐feedback control approach is further derived by adopting an input‐driven filter and conducting trivial changes on the design procedure. Moreover, both approaches exhibit significant simplicity, without the needs for parameter identification, function approximation, disturbance estimation, derivative calculation, or command filtering. Three simulation studies are conducted to clarify and verify the above theoretical findings.

Pose Selection Based on a Hybrid Observation Index for Robotic Accuracy Improvement

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.

Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Optimizing near-carbon-free nuclear energy systems: advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence

Corn steep liquor is a waste product from the process of treating corn grain for starch extraction. It is used as a substrate in anaerobic digestion with simultaneous hydrogen and methane production in a cascade of two anaerobic bioreactors. For process research and optimisation, adequate mathematical models are required. So, the authors aim to present a high-quality model of the corn steep liquor process for the sequential production of H2 and CH4. This paper proposes a technique for identifying the best mathematical model of the process using the metaheuristics crow search algorithm (CSA). The CSA was applied for the first time to mathematical modelling of the considered two-stage anaerobic digestion process, using real experimental data. Based on the analysis of the numerical data from the model parameter identification procedures, the influence of the main CSA parameters—the flight length, fl, and the awareness probability, AP—was investigated. Applying classical statistical tests and an innovative approach, InterCriteria Analysis, recommendations about the optimal CSA parameter tuning were proposed. The best CSA algorithm performance was achieved for the AP = 0.05, fl = 3.0, followed by AP = 0.10, fl = 2.5, and AP = 0.15, fl = 3.0. The optimal tuning of the CSA parameters resulted in a 29% improvement in solution accuracy. As a result, a mathematical model of the considered two-stage anaerobic digestion process with a high degree of accuracy was developed.

Parameter fuzzy rectification for sliding mode control of five-phase permanent magnet synchronous motor speed control system

Introduction: Nowadays, five-phase permanent magnet synchronous motors have been widely used in the industrial and transportation fields, and the existing sliding mode control methods for speed control systems can no longer meet the requirements such as fast response and good stability.Methods: In light of the aforementioned considerations, the study initially employs mathematical modeling to elucidate the five-phase permanent magnet synchronous motor. Secondly, on the basis of proportional-integral-derivative sliding mode control, radial basis function and Takagi-Sugeno-Kang fuzzy model are introduced for parameter identification and optimization and regulation. Finally, a new neural network regulation algorithm and speed control strategy are proposed.Results and Discussion: The experimental results demonstrated that the expected parameter optimization rate of the regulation algorithm can reach 90%, and the overshooting amount under small inertia working condition is only 3%, and the adjustment time is 0.02 s. The new control algorithm can be used to control the motor speed with the lowest speed fluctuation and the fastest recovery time. In addition, when affected by the load torque, the motor speed controlled by the new strategy fluctuated the least, with a speed drop of only 1% and the fastest recovery time of 0.02 s. It exhibited the lowest control error of 3.7% and the lowest overshooting amount of 5.9%.Conclusion: In summary, the suggested approach has the potential to significantly enhance the speed control system’s control performance while maintaining strong resilience and anti-interference capabilities. The method has certain guiding significance for the practical application of five-phase permanent magnet synchronous motor speed control system.

Identification, uncertainty quantification and validation of orthotropic material properties for additively manufactured polymers

The mechanical behavior of polymeric parts manufactured by fused filament fabrication is often characterized as orthotropic because of the layer-wise extrusion of the material. To perform reliable simulations of the mechanical response of these parts subjected to certain loads, the material parameters have to be known. This leads to the task of material parameter identification from suitable experimental data. In this work, the identification of the material parameters for orthotropic material behavior is studied in detail with a focus on the specific characteristics of additively manufactured parts. This comprises working out the differences that arise compared to the parameter identification for classical orthotropic composite materials. Moreover, the material parameters are identified from tensile and shear test information together with full-field displacement data from digital image correlation measurements. Then, the obtained parameters are compared for analytical and numerical approaches, where a non-linear least-squares method with finite elements is drawn on. The parameters are not only identified, but also an uncertainty analysis is carried out and, subsequently, the different methods are validated. Further, a clear derivation of commonly applied analytical equations is provided with a special focus on the underlying assumptions. It turns out that the analytical parameter identification procedures can not be easily transferred to numerical procedures, especially when using full-field data, because of correlations between the parameters. Moreover, the identified parameters are employed to prove the reasonability of orthotropic material behavior for specimens manufactured by fused filament fabrication. Finally, during validation it turns out that all parameter identification methods provide a good agreement with experimental data, showing only small differences between the results of the different calibration strategies.