Multi-parameter Identification Research Articles

Multiparametric identification of putative senescent cells in skeletal muscle via mass cytometry.

Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI-67. The characterization of different senescence-associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single-cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry-based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well-established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy-related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence-related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL-6. Overall, the analysis framework prioritizes multiple senescence-associated proteins to characterize putative senescent cells sourced from tissue made of different cell types.

Online Decoupled Multi-Parameter Identification of Dual Three-Phase IPMSM Under Position-Offset and HF Signal Injection

Accurate and updated knowledge of electrical parameters is of great importance for high-performance control of motor drives. However, the common challenging issue in existing identification methods is to construct a full-rank model with low parameter coupling. In this paper, an online multi-parameter identification method has been proposed for the dual three-phase interior PMSM (IPMSM) drives with position-offset and high-frequency square-wave voltage injection. Thanking to the simplified amplitude extraction process, the inductance parameters can be conveniently calculated from HF current amplitude under HF square-wave voltage injection. On this basis, the position-offset injection based model has been developed for the individual identification of the rotor flux linkage. Finally, the resistance can be estimated according to the steady-state voltage equations in harmonic subspace with the aid of known inductance. With the constructed multi-source identification models, the rank-deficient problem of multi-parameter estimation can be solved. Furthermore, the additional control dimensions of dual three-phase IPMSM have been utilized to avoid the torque disturbance from position-offset injection. The experimental results are presented to verify performance of the proposed method.

Multi-parameter identification of earthquake simulation shaking table based on BP neural network

Since the model parameters of the shaking table exist in a non-linear form, this leads to distortion of the reproduced waveforms and can even lead to bias in the ground vibration test results. Therefore, the selection of the controller is particularly critical. Multi-variable (MVC) controllers are often used in shaking table control, to improve the control effect of MVC controllers. In this paper, a multi-parametric (BP-MVC) controller based on BP neural network is proposed. The BP neural network is applied to the multi-parameter (MVC) controller to identify the shaking table model, adjust the parameters in real-time, accelerate the convergence speed, and reduce the system error. The simulation results show that the correlation coefficient (CC) of the BP-MVC controller is greater than 0.985, and the root-mean-square error (RMSE) and mean absolute error (MAE) are less than 0.04 and 0.25, respectively, in a nonlinear, time-varying hydraulic system. This suggests that the BP-MVC controller has a better control performance and parameter adaptivity, which can provide a reference for the subsequent ground vibration tests.

Effects of Triangular Wave Injection and Current Differential Terms on Multiparameter Identification for PMSM

Effects of Triangular Wave Injection and Current Differential Terms on Multiparameter Identification for PMSM

Multiparameter Identification for Active Magnetic Bearing With Uncertainties Based on a Coupled Nonlinear Model

Parameter identification enables active magnetic bearing (AMB) to obtain more state information, which can be used for control optimization, condition monitoring and fault diagnosis for rotating machinery. Due to the limitation of working principle and modeling accuracy, the traditional parameter identification methods show inherent disadvantages in cost, applicability or precision. Thus, this paper proposes an accurate multi-parameter identification method without any instrumentation. This method takes into account the electromagnetic force coupling caused by the asymmetric magnetic circuit, the non-linearity caused by the saturation effect, together with the uncertain parameters caused by machining and assembly errors, temperature, etc. Firstly, the coupled nonlinear magnetic circuit model of the AMB is established by the reluctance network method, and the influence of non-ideal factors on the electromagnetic force is treated. Secondly, a novel experimental method for parameter identification is presented. According to the equilibrium relationship between the electromagnetic force and the supporting load, the force balance equations of the rotor under different working conditions are obtained. Taking the supporting parameters and uncertain geometric parameters as the parameters to be identified, the multi-parameter identification model is established. Finally, the proposed method is validated by the finite element method (FEM) and experiments. The results show that the method can accurately identify the air gap length, rotor origin, static supporting load, and stiffness of the AMB.

A novel method for simultaneous determination of thermophysical properties and boundary conditions of phase change problems based on element differential method

Thermophysical properties and boundary conditions for phase change thermal management systems are challenging to be accurately determined, due to the phase change heat transfer phenomenon and complex working conditions. In this work, the element differential method (EDM) and a gradient-based method are combined to simultaneously predict thermal conductivity, mass specific heat, and boundary heat flux in two-dimensional (2D) and three-dimensional (3D) inverse phase change problems, for the first time. The multi-parameter identification for the 3D physical model with phase change is more general than the previous attempts. Moreover, the effective heat capacity method is employed to deal with phase change problems, to improve efficiency. The sensitivity coefficient is accurately determined by the complex-variable-differentiation method (CVDM) in the multi-parameter prediction. Finally, the effect of measurement points, measurement errors, and initial guessed values on the multi-parameter identification are investigated. This study demonstrates that the present method has good accuracy, efficiency, stability, and robustness in dealing with transient nonlinear inverse problems during phase change process.

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7.

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

Sources of Cross-Polarized Radiation in Microstrip Patches: Multiparametric identification and insights for advanced engineering

This article revisits the reasons behind the cross-polarized (XP) radiation in microstrip patches and focuses on alleviating a long-standing deficiency in terms of a comprehensive knowledge about the same. This study has a twofold objective. First, it explores all possible components of the surface fields as the correlating factors over all of the radiation planes, especially across the diagonal or skewed axes. An extensive study involving varied patch geometries along with different feed networks has been executed. The resulting huge volume of data has been analyzed to identify the ideal surface field requirements for the best possible XP performance.

Adjacent-Vector-Based Model Predictive Control for Permanent Magnet Synchronous Motors With Full Model Estimation

The finite-control-set model predictive control (FCS-MPC) shows appealing features in permanent magnet synchronous motor control but suffers from high current ripples. Multiple-vector-based MPC (MVB-MPC) methods reduce the ripples but may lose some of the merits possessed by the FCS-MPC. Furthermore, the performance of both the FCS-MPC and the MVB-MPC is dependent on the accurate knowledge of the system parameters. This article proposes an adjacent-vector-based MPC (AVB-MPC) combining the FCS-MPC and the MVB-MPC. Instead of finding a solution with minimum cost, the AVB-MPC searches from a lookup table of adjacent vectors for the solution with the least switching actions to keep the cost within a tolerable band. Hence, the current ripple is reduced under the same switching frequency, while the merits of fast dynamic responsiveness and inherent overmodulation ability are retained. In addition, a full model estimation method is proposed so that the knowledge of the machine parameters is not required. The rank deficiency problem of multiparameter identification is also solved by the tolerable band. And these benefits are gained without adding much computational effort. Finally, the effectiveness and benefits of the AVB-MPC are validated by comparative experimental results comparing with several other typical predictive controllers.

Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm

This paper presents a novel methodology that combines polynomial chaos expansion and slime mould algorithm for multi-parameter identification of concrete dams. This methodology not only incorporates the merits of low computational cost in the polynomial chaos expansion and fast convergence of slime mould algorithm, but also considers the priori uncertainty in the input parameters by introducing statistical probability theory. By considering two examples with different complexity, this paper verifies the effectiveness of the proposed method with a univariate simply supported beam model, followed by a complex multivariate dam model to demonstrate its practicability in real engineering problems. In addition, parameter sensitivity analysis of the dam model is conducted at an extremely low cost by polynomial chaos expansion based on Sobol’ indices. Furthermore, the conventional parameter identification methods based on optimization methods directly combined with the finite element model are employed for comparison, highlighting two distinct advantages of the proposed method: (i) the proposed method improves the computational efficiency by nearly 52 times while ensuring a high accuracy, and (ii) the classical non-population optimization algorithm, Bayesian optimization, is used for comparison, revealing the outstanding performance of slime mould algorithm in terms of convergence speed and robustness. The application of the proposed algorithm is not only limited to dams, but also it can be extended to any structure.

Multifrequency-based tension intelligent identification for cables with unknown end-restraints using a metaheuristic algorithm

Vibration frequency methods are widely used for cable tension identification; however, calibrated a priori knowledge of the flexural stiffness, axial stiffness and end-restraints of cables cannot always be obtained in advance, leading to the inabilities of these methods in the identification from theoretical or empirical formulations. In this study, a multifrequency-based intelligent multiparameter identification method is proposed for the tension identification of a cable equipped with a single sensor. The framework of the method establishes an optimization objective function with the theoretical and on-site frequencies based on a cable dynamic model considering the effects of inclination, sagging, flexural stiffness and end-restraints. The identification domain is obtained from empirical formulas and dimensionless characteristic parameters, and the model is solved using a finite difference method. An efficient metaheuristic algorithm is introduced as an engine for the simultaneous identification of the cable tension and the parameters supposed to be calibrated first. The accuracy and efficiency of the method are demonstrated through comparative studies consisting of algorithm tests, tension identification tests of laboratory rigid short cables and some stay-cables of an on-site bridge.

Research on the multi-parameter identification of thermal-acoustic-solid coupling problem in an inhomogeneous medium

This work considers the multi-parameter identification problem in initial-boundary values of variable coefficient coupled partial differential equations. Based on ultrasonic echo time measurements, we identify the surface heat flux and thickness simultaneously, and then achieve the reconstruction of the internal transient temperature field. Through rigorous theoretical analysis, we show the uniqueness recovery result for the multiparameter identification problem. By reformulating the inverse problem as an optimization problem, we propose an efficient alternating iteration algorithm in solving the related optimization problem and provide a rigorous convergence analysis of the algorithm. Finally, the reliability and feasibility of this algorithm are verified by some numerical examples.

Second-Order ESO-Based Current Sensor Fault-Tolerant Strategy for Sensorless Control of PMSM With B-Phase Current

Usually, two-phase current sensors are required to realize sensorless control of permanent magnet synchronous motor (PMSM). However, once the phase current sensor fails, the whole system will not work. In this article, a novel second-order extended state observer (SO-ESO) based current sensor fault-tolerant strategy for realizing PMSM sensorless control and multiparameter identification is proposed, which can significantly improve the system reliability. When a fault occurs in the A-C two-phase current sensor, the proposed scheme uses only a B-phase current sensor to estimate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> axis stator current and time varying resistance, achieving a high-performance sensorless control function. In order to reduce the chattering of the system, a sigmoid function is introduced in the SO-ESO. In addition, an SO-ESO based parallel speed and multiparameter identification scheme are proposed by using two model reference adaptive system observer (MRASO). The first MRASO is designed based on super twisting algorithm with a corrective feedback loop and is used for estimating rotor speed and position. The second MRASO only needs to identify inductance and permanent flux because the stator resistance is estimated by SO-ESO. The second MRASO and SO-ESO updates the three identified electrical parameters (i.e., stator resistance, inductance, and permanent flux) and estimated currents to the first MRASO to achieve high robustness sensorless control. The relevant results show that the scheme gives good results, and the system has good dynamic performance and better fault tolerance.

Improved Small-Signal Injection-Based Online Multiparameter Identification Method for IPM Machines Considering Cross-Coupling Magnetic Saturation

A novel online multiparameter identification method is proposed for interior permanent magnet (IPM) machines considering cross-coupling magnetic saturation. The identification method is based on the combination of small-signal impedance model and fundamental frequency signal model under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axes. To analyze the nonlinearity of IPM machines, cross-coupling magnetic saturation effect is introduced into online multiparameter identification for the first time. Based on this model, a phase compensation strategy and orthogonal decomposition in phasor domain is proposed to decouple the complicated mathematical model into four linear equations. By injecting voltage small-signals into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axes, multiparameter of IPM machine including adjusted apparent inductance, incremental self-inductance, incremental mutual-inductance can be identified online in a wide operation region. During this process, no additional computing equipment is required. The proposed method is carried out on a tested IPM machine and verified by the finite-element analysis.

Multiparametric identification of subclinical atrial fibrillation after an embolic stroke of undetermined source.

Subclinical atrial fibrillation (SCAF) may represent a cause of embolic stroke of undetermined source (ESUS) and its detection has important implications for secondary prevention with anticoagulation. Indications to implantable cardiac monitors (ICM) include SCAF detection. The aims of this study were to (1) evaluate the frequency of ICM-detected SCAF; (2) determine predictors of SCAF; and (3) identify patients who would benefit most from ICM implantation. Between February 2017 and November 2020, all consecutive patients referred for ICM implantation after a diagnosis of ESUS and without previous history of atrial fibrillation or atrial flutter were included in this study. SCAF was diagnosed if the ICM electrogram demonstrated an episode of irregularly irregular rhythm without distinct P waves lasting > 2min. We enrolled 109 patients (age 66, SD = 13years; 36% females). During a median follow-up of 19.2 (IQR 11.0-27.5) months, SCAF episodes were detected in 36 (33%) patients. Only abnormal P wave terminal force in lead V1, left atrial end-systolic indexed volume > 34ml/m2, and BMI > 25kg/m2 were independently associated with an increased risk of SCAF (HR 2.44, 95% CI 1.14-5.21, p = 0.021; HR 2.39, 95% CI 1.11-5.13, p = 0.026; and HR 2.64, 95% CI 1.06-6.49, p = 0.036 respectively). The ROC curve showed that the presence of all three parameters had the best accuracy (74%) to predict SCAF detection (sensitivity 39%, specificity 91%). A multiparametric evaluation has the best accuracy to predict SCAF in ESUS patients and may help identifying those who would benefit most from ICM.

An investigation into the multi-parameter identification and model optimization strategy of the PCT curves of hydrogen storage alloys by multiple intelligent algorithms

A multi-parameter sensitivity analysis method was proposed based on Latin hypercube sampling and the rank correlation method. The general genetic algorithm was improved by incorporating a niche parallel algorithm, suspicious peak point judgment, and a local search strategy, and both the improved genetic algorithm and multi-parameter sensitivity analysis method were implemented through C++ programming. The hydrogen pressure-composition isotherm (PCT curve) was found to play an important role in the study of hydrogen storage properties of metallic materials, and its reaction enthalpy change term was widely replaced by a power series form of smoothing. Based on the improved multi-parameter sensitivity analysis method, the effects of the range of parameter values and the number of samples on sensitivity were investigated, where the sensitivity ranking of the 10 parameters in the classical model was obtained. In addition, the equilibrium hydrogen pressure expression models of the highest order i of the power series from 3 to 9 were developed. The influence of the highest order on the optimization of multi-parameter identification was compared using an improved genetic algorithm, and we concluded that the eighth order polynomial model had the optimal convergence and fitting effect. Based on the inspiration of the function trend shape, two innovative forms of the PCT model power series were proposed in this work. Combined with typical examples, three high-order models were fully evolved, and a better power series form was obtained compared to the traditional model.

A new ultrasonic method for synchronous measurement of internal temperature distributions and thickness in high-temperature structures

A new ultrasonic method for synchronous measurement of multiple parameters of heated materials is proposed in this paper. Based on the thermal-acoustic-solid coupling theory, the internal temperature distributions and thickness of solid structures are reconstructed synchronously by an alternate iterative multi-parameter identification method. Firstly, a numerical model of multi-parameter synchronous identification is obtained based on temperature dependence of the velocity of the ultrasonic wave propagating through the heated material. Then, an inverse analysis by the alternate iterative method for estimating equivalent thermal boundary conditions and thickness. In addition, experiments are carried out in order to validate the feasibility and reliability of the developed method, whose stability and accuracy are also analyzed through a comprehensive parameter analysis. The research results show that the structure thickness obtained by the alternate iteration method based on ultrasonic transit time accords with the physical reality and the internal transient temperature profiles determined ultrasonically agrees well with those obtained using thermocouples installed in the steel. It demonstrates that the method proposed in this work is an effective multi-parameter synchronous measurement means to determine the internal transient temperature distribution and thickness of high temperature structures with high accuracy by ultrasonic pulse echo measurements.

Reverse Nonlinear Sparrow Search Algorithm Based on the Penalty Mechanism for Multi-Parameter Identification Model Method of an Electro-Hydraulic Servo System

Aiming at the multi-parameter identification problem of an electro-hydraulic servo system, a multi-parameter identification method based on a penalty mechanism reverse nonlinear sparrow search algorithm (PRN-SSA) is proposed, which transforms the identification problem of a non-linear system into an optimization problem in a high-dimensional parameter space. In the initial stage of the sparrow search algorithm (SSA), the population distribution is not uniform, and the optimization process is easily disturbed by the local optimal solution. First, adopting a reverse learning strategy increases the exploratory nature of individuals in a population, improves population diversity, and prevents premature maturity. Subsequently, a flexible strain mechanism is provided through the nonlinear convergence factor, adaptive weight factor, and golden sine and cosine factor. The introduction of a nonlinear factor fully balances the global search and local development abilities of the algorithm. Finally, a punishment processing mechanism is developed for vigilantes while retaining the population, providing a suitable search scheme for individuals beyond the boundary, and making full use of the value of each sparrow individual. The effectiveness of each improved strategy is verified through simulation experiments with 23 benchmark functions, and the improved algorithm exhibits better robustness. The results of the model parameter identification of the electro-hydraulic servo system show that the method has a high fitting accuracy between the identification model data and the experimental data, and the fitting degree of the identification model exceeds 97.54%, which further verifies the superiority of the improved algorithm and the effectiveness of the proposed identification strategy.

Multiparametric Identification of Favorable Regions for Wind or Solar Generation in the State of Pernambuco

The capacity for renewable energy generation, particularly solar and wind, tends to grow over the years. However, locating regions favorable to the development of solar and wind generation is not trivial and is subject to errors due to the social, environmental, economic, political and administrative aspects of the regions. Therefore, this article proposes an algorithm based on digital image processing, capable of performing a variable weight multi-parameter evaluation and quantifying how favorable a given territorial region is for the implantation of wind or solar plants. Maps of the region under analysis are taken as input information. The region taken as a case study is the Brazilian state of Pernambuco, due to the access to data related to the evaluated characteristics: solar radiation, solar insolation, precipitation, air humidity, temperature, electrogeography, demographic density, environmental protection areas, slope, hydrography, wind speed, urban spot and the presence of aerodromes. It was possible to list for each type generation (wind or solar, exclusively), the 15 most favorable regions for the implementation plants. Furthermore, the proposed technique presents a practical indifference to the territorial extension of the evaluated region, which can comprehend a micro-region or an entire continent, requiring only that sufficiently detailed maps of the evaluated characteristics be provided.

An integrated calibration technique for variable-boresight three-dimensional imaging system

There is an emerging need for multiview stereo systems achieved with high accuracy, wide coverage, and flexible perspective in spatially-limited vision and close-range photogrammetry. Accurate and efficient calibration is a critical prerequisite that is always performed to ensure high-performance object reconstruction and situational awareness. This paper proposes an integrated calibration technique based on rigorous modeling for the variable-boresight three-dimensional imaging system composed of a single camera and a wedge prism. A unified self-calibration framework which considers the misalignment between camera and prism is systematically demonstrated such that all involved parameters are estimated by minimizing the statistical discrepancy between model prediction and reference information. Moreover, a novel approach is presented to construct calibration reference through adaptive generation and flexible transfer. Experiments have validated that the proposed technique can offer a viable solution to multi-parameter identification for the imaging system, which therefore contributes to the recovery of any spatial object with competitive geometric accuracy and satisfactory robustness to potential error sources.