Multivariable Identification Research Articles

Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability

The controller is important for the artificial pancreas to guide insulin infusion in diabetic therapy. However, the inter- and intra-individual variability and time delay of glucose metabolism bring challenges to control glucose within a normal range. In this study, a multivariable identification based model predictive control (mi-MPC) is developed to overcome the above challenges. Firstly, an integrated glucose-insulin model is established to describe insulin absorption, glucose-insulin interaction under meal disturbance, and glucose transport. On this basis, an observable glucose-insulin dynamic model is formed, in which the individual parameters and disturbances can be identified by designing a particle filtering estimator. Next, embedded with the identified glucose-insulin dynamic model, a mi-MPC method is proposed. In this controller, plasma glucose concentration (PGC), an important variable and indicator of glucose regulation, is estimated and controlled directly. Finally, the method was tested on 30 in-silico subjects produced by the UVa/Padova simulator. The results show that the mi-MPC method including the model, individual identification, and the controller can regulate glucose with the mean value of 7.45 mmol/L without meal announcement.

Data-driven multivariate identification of gyrification patterns in a transdiagnostic patient cohort: A cluster analysis approach

BackgroundMultivariate data-driven statistical approaches offer the opportunity to study multi-dimensional interdependences between a large set of biological parameters, such as high-dimensional brain imaging data. For gyrification, a putative marker of early neurodevelopment, direct comparisons of patterns among multiple psychiatric disorders and investigations of potential heterogeneity of gyrification within one disorder and a transdiagnostic characterization of neuroanatomical features are lacking. MethodsIn this study we used a data-driven, multivariate statistical approach to analyze cortical gyrification in a large cohort of N = 1028 patients with major psychiatric disorders (Major depressive disorder: n = 783, bipolar disorder: n = 129, schizoaffective disorder: n = 44, schizophrenia: n = 72) to identify cluster patterns of gyrification beyond diagnostic categories. ResultsCluster analysis applied on gyrification data of 68 brain regions (DK-40 atlas) identified three clusters showing difference in overall (global) gyrification and minor regional variation (regions). Newly, data-driven subgroups are further discriminative in cognition and transdiagnostic disease risk factors. ConclusionsResults indicate that gyrification is associated with transdiagnostic risk factors rather than diagnostic categories and further imply a more global role of gyrification related to mental health than a disorder specific one. Our findings support previous studies highlighting the importance of association cortices involved in psychopathology. Explorative, data-driven approaches like ours can help to elucidate if the brain imaging data on hand and its a priori applied grouping actually has the potential to find meaningful effects or if previous hypotheses about the phenotype as well as its grouping have to be revisited.

Identification and Optimization of County-Level Ecological Spaces under the Dual-Carbon Target: A Case Study of Shaanxi Province, China

County-level ecological space, as a crucial level in optimizing the land spatial system, plays a pivotal role in “undertaking superior planning and guiding subordinate implementation”. From a spatial optimization perspective, effectively implementing the dual-carbon goal by increasing carbon sinks in specific ecological space units is essential. This study focused on 107 districts and counties in Shaanxi Province, China, aiming to construct a comprehensive multivariate identification system for ecological space under the dual-carbon target based on an analysis of the spatiotemporal distribution characteristics and driving factors of county-level carbon sinks. Furthermore, by analyzing the ecological spatial distribution pattern, carbon sink land structure, and county clustering characteristics, the study explored differential optimization strategies for ecological spaces of different county types to enhance carbon sinks in the ecosystem. The results demonstrated that: (1) From 2000 to 2020, the total carbon sink in Shaanxi Province exhibited an initial increase followed by a decrease, with a decline from 864.39 × 104 t to 863.21 × 104 t. The county-level distribution of total carbon sink displayed significant spatial heterogeneity, with an overall pattern of south > north > central. (2) The interaction among factors enhanced the explanatory power for spatial differentiation of county-level carbon sinks compared to individual factors, exerting an important impact on the spatial distribution pattern of carbon sinks. (3) The distribution of ecological space in Shaanxi Province was highly uneven, with the core ecological space primarily concentrated in the southern and north-central regions. The proportions of low carbon sink (Type I), medium carbon sink (Type II), and high carbon sink (Type III) counties were 35.51%, 18.69%, and 45.80%, respectively. For different types of county-level ecological spaces, this study proposed a differentiated optimization strategy aimed at reducing carbon emissions and enhancing carbon sink. The results will provide theoretical and technical support for regional ecological construction and land spatial optimization, holding significant practical implications for achieving the dual-carbon goal and addressing climate change.

A gradient based numerical algorithm to solve inverse dynamic viscoelastic problems of multi-variable identification

A gradient based numerical method is put forward to solve the inverse dynamic viscoelastic problem with multi-variable identification for the constitutive and TKBC (third kind boundary conditions) related parameters. A three parameter model is employed to describe the time dependent TKBC concerned with structural interaction, and is formulated in the forward and inverse dynamic viscoelastic analysis. In addition to the reliable platform provided by TPAA-SBFEM to gain the solution of forward problems, a piecewise recursive adaptive algorithm is developed to obtain the derivatives required in the sensitivity analysis, which is able to provide a stable temporal solution accuracy for different step sizes. SBFEM (Scaled Boundary Finite Element Method) is stressed in the structural analysis with its own advantages, such as the efficient and convenient treatment of stress singularity problem and flexibility of mesh generation with polygonal elements and image based quadtree gridding. The inverse problem of identification is solved by the Levenberg-Marquardt method, and the regional inhomogeneity, crack, initial guess, location/number of measurement points, material contrast ratios, and noisy data are taken into account. Numerical examples are provided to verify the proposed approach, and the satisfactory results are achieved.

Multivariable identification of membrane fouling based on compacted cascade neural network

The system consists of three modules: data acquisition, feature extraction, and online prediction. The data acquisition module obtains the value of process variables from the sensor and transfers the acquired variables to the database via PLC. The feature extraction module is designed to filter the collected process data. In this module, multiple related variables are selected as the preselected variables. Then the PLS method is used to reduce the dimension of the preselected variables to obtain the feature variables with a high correlation with the predicted variables. Finally, the online prediction module based on a cascade neural network predicts membrane fouling, membrane integrity, and membrane life for clean decisions. • A multivariable identification model is construted. • Cascade neural network is proposed to predict multiple variables to avoid the interference of overlap inputs. • Unsupervised pretraining algorithm is used to initialize the structure of cascade neural network. • Because the parameters are affected by multiple errors, the hierarchical learning algorithm is used to improve the identification accuracy. The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor (MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model (MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM. Thirdly, a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online. Finally, the proposed model was tested in real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

Mathematical analysis of the effect of process conditions on the porous structure development of activated carbons derived from Pine cones

This paper presents the results of a study on the influence of the degree of impregnation and activation temperature on the formation of the porous structure of activated carbons (ACs) obtained from Pine cones by the chemical activation process using potassium hydroxide as an activator. The advanced new numerical clustering based adsorption analysis (LBET) method, together with the implemented unique numerical procedure for the fast multivariant identification were applied to nitrogen and carbon dioxide adsorption isotherms determined for porous structure characterization of the ACs. Moreover, the Quenched Solid Density Functional Theory (QSDFT) method was chosen to determine pore size distributions. The results showed a significant influence of the primary structure of Pine cones on the formation of the porous structure of the developed ACs. Among others, it was evidenced by a very high degree of surface heterogeneity of all the obtained ACs, irrespective of the degree of impregnation with potassium hydroxide and the activation temperature. Moreover, the analysis of carbon dioxide adsorption isotherms showed, that the porous structure of the studied ACs samples contains micropores accessible only to carbon dioxide molecules. The results also showed a significant advantage of the LBET method over those conventionally used for porous structure analysis based on Brunauer–Emmett–Teller (BET) and Dubinin–Raduskevich (DR) equations, because it takes into account surface heterogeneities. The novel analyses methods were more fully validated as a reliable characterization tool, by extending their application to the isotherms for ACs developed from the same precursor by phosphoric acid activation, and for samples arising from these ACs, further subjected to additional post-treatments. The effect of the raw material used as precursor was moreover analysed by comparison with previous reported results for other ACs. The complementarity of the results obtained with the LBET and QSDFT methods is also noteworthy, resulting in a more complete and reliable picture of the analyzed porous structures.

Modeling and development of a low-cost didactic plant for teaching in multivariable systems

This paper aims the planning, construction and modeling of a low-cost multivariable level plant for didactic purposes. The developed model has two pumps that feed four tanks coupled to each other. Between the tanks and the pumps, there are inlet and outlet valves that can change the system dynamics according to its opening configuration. To automate the pumps and read the instrumentation used, the Arduino microcontroller was chosen because it is a model of great use in the academic environment and of easy parameterization. For sensing, the HC-SR04 ultrasonic sensor was chosen, which already has native compatibility with the microcontroller. In order to validate the constructed plant, it was necessary to identify its model, using the empirical step response method for this purpose. In this way, this work has both qualitative and quantitative characteristics, since the planning and construction of the didactic plant involved an exploratory research of the problem, and then the modeling and simulation method was applied to obtain the mathematical model of the plant. Finally, an experimental research was conducted, comparing the data obtained in the real plant with the model data for validation. Having completed all research stages, the work result is a didactic plant with good linearity, able to provide implementation of level control strategies of coupled tanks and to assist in teaching and learning subjects that involve concepts of dynamic systems, besides multivariable systems identification and control.

Numerical analysis of the micropore structure of activated carbons focusing on optimum CO2 adsorption

In this work, the porous structure of activated carbons prepared from Moso bamboo were mathematically analysed. At first, the raw material was subjected to two-stage activation process i.e. in the first stage, phosphoric acid or zinc chloride was used followed by second stage physical activation or chemical activation with potassium hydroxide. Then, nitrogen and carbon dioxide adsorption isotherms were determined. Subsequently, a novel, clustering-based adsorption analysis method was applied, combined with a numerical procedure for the fast multivariant identification of adsorption systems. The mentioned method takes into consideration the heterogeneity of the adsorbent surface and allows the determination of the adsorption energy as well as the shape and size of the clusters of adsorbate molecules formed in the pores of the analysed material. Additionally, the Quenched Solid Density Functional Theory was selected to determine the cumulative pore volume, pore size distributions and other micropore parameters. The analyses revealed a considerable degree of complexity of the carbons’ porous structure, including differences in the adsorption of CO2 and N2. The complementarity of the applied methods leads to the reliable determination of the optimum activated carbon for CO2 adsorption and assists reverse engineering approaches in the production and activation stage.

Identification-based real-time optimization and its application to power plants

Most real-time optimization (RTO) methods in process industries are based on rigorous (mechanistic) models or on some special performance tests. Both methods are difficult to apply due to low model accuracy and high costs in modeling, in computation or in performance tests. In this work, a real-time optimization method based on system identification is developed. No rigorous models or special performance tests are needed in this approach, making it cost-effective and applicable in various process industries and especially suitable for energy and utility systems. First, the selection of objective function and of decision variables is discussed. Then the identification-based optimization method is proposed where a closed-loop multivariable identification approach is incorporated. The method is verified using a simulated power plant and is also partially applied to a real power plant.

HIGH-PERFORMANCE MODELING, IDENTIFICATION AND ANALYSIS OF HETEROGENEOUS ABNORMAL NEUROLOGICAL MOVEMENT’S PARAMETERS BASED ON COGNITIVE NEURO FEEDBACK-INFLUENCES

The technique is based on a hybrid model of the neuro-system (Brain cortex nodes and tremor-object), which describes on the basis of wave signal propagation the state and behavior of tremor-objects T, namely the segmental de-scription of 3D elements of trajectories of anormal neurological movements of the studied tremor-objects (limb of the hand) taking into account the matrix of cogni-tive influences of groups of cortex neuro-nodes. The rapid analytical solution of the model as a vector function that describes the 3D elements of the trajectories at each movements segment are constructed using the hybrid integral Fourier’s transformations and hybrid spectral function. The main element of the solution is the adaptive infuences matrix that determines the state parameters of the action of certain groups of brain neuro- cortex. Models and methods of multivariable identification are being developed to investigate their neuro-feedback, which suggest high-speed parallel computations on multicore computers. This model-ing technology consider as a scientific basis for designing inelidgence information systems of the quality medical diagnostic i of critical neurological diseases.Key words: Computer simulation, Software system, High-performance compu-ting, Tremor diseases, Modeling of objects and processes, Multi-parameter iden-tification.

Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing.

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

A 3×3 visible-light cross-reactive sensor array based on the nanoaggregation of curcumin in different pH and buffers for the multivariate identification and quantification of metal ions

Here, a facilely constructed 3 × 3 visible-light cross reactive sensor array based on nanoaggregation of curcumin (Cur) is proposed for the identification and quantification of metal ions (MIs). Synthesis of nanocurcumin (NCur) was characterized by UV–Vis spectrophotometry, transmission electron microscopy (TEM) and Fourier transform infrared (FT-IR). The average particle size was estimated about 5.21 ± 1.13 nm) n = 50 (. Our sensor array consists of nine receptors with distinct but overlapping specificities for 11 MIs: Al3+, Cd2+, Co2+, Cu2+, Hg2+, Fe2+, Fe3+, Mn2+, Ni2+, Pb2+, and Zn2+. The receptors include the nine solutions of NCur at three buffers of phosphate, ammonium, and tris each at three pH of 7, 8, and 9 (in total 9 receptors). On account of different pH and buffers, NCur-MI binding affinities can be distinguished by monitoring the UV–Vis absorbance changes. These changes are optical fingerprints that can be used to identify each MI. The absorption values in sixteen wavelengths (i.e. 332, 352, 372, 392, 412, 432, 452, 472, 492, 512, 532, 552, 572, 592, 612, and 632 nm) are considered as analytical signals to quantitatively evaluate of the absorbance responses of the sensor array. A color difference map is provided to qualitatively visualize of the colorimetric sensor array responses. Under optimal conditions, the MIs are successfully discriminated in the range of 4–48 μmol L−1. The limit of detections (LODs) values ranged from 0.47 (for Fe3+) to 1.40 μmol L−1 (for Pb2+). Furthermore, two different mixing sets of the MIs are prepared for multivariate multicomponent analysis. Finally, the suggested sensor array is employed to evaluate its practicability in the discrimination of MIs in samples of river water and serum. Moreover, it can identify the MIs in these samples. The sensor array presents a simple, save time, cost-effective, and environmentally friendly method for the identification and quantification of MIs.

Fractionation of chain walking polyethylene and elucidation of branching, conformation and molar mass distributions

State-of-the-art liquid interaction chromatography (IC) allows separation of macromolecules according to chemical composition (CC), functionality or molecular topology in contrast to size-based approaches such as size exclusion chromatography (SEC), which has its limitations in distinguishing chemical structures. Yet, for macromolecules with multivariate molecular distributions, online identification and characterization of distinct components in bulk samples is a considerable challenge. A useful approach to narrow down the complexity of a given sample is preparative molar mass fractionation (pMMF) that provides fractions in mg or g scale, which can be subjected to further advanced analysis to elucidate the physical and structural properties of the sample. In this study, novel chain-walking polyethylenes (CWPE), which differ substantially in macromolecular architecture, are fractionated using pMMF. Using IC in temperature and solvent gradient modes (TGIC, SGIC), the pMMF fractions are separated according to branching topology. Quadruple-detector high temperature SEC is carried out to determine the physical properties and study the MM-dependent structural transformation of CWPE. A detailed branching analysis was carried out by means of nuclear magnetic resonance. Two-dimensional liquid chromatography (LC) hyphenating IC and SEC provides comprehensive bivariate correlations between MM and molecular topology.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

A noise-tolerant model parameterization method for lithium-ion battery management system

A well-parameterized battery model is prerequisite of the model-based estimation and control of lithium-ion battery (LIB). However, the unexpected yet inevitable noises may markedly discount the identification of model parameters in real applications. This paper focuses on the noise-immune and unbiased model parameter identification for LIB. The signal-disturbance interface in LIB model identification is firstly analyzed by reformulating an overdetermined nonlinear system, on the premise of a cautiously-designed instrumental vector estimator. The multi-variable identification is then solved in the framework of a separable nonlinear least squares (SNLS) problem via a novel two-step method combining least squares (LS) and variable projection algorithm (VPA), to co-estimate the noise variances and unbiased model parameters. A numerical solver is further exploited for the proposed LSVPA, giving rise to a recursive and computational efficient algorithmic architecture which is favorable for online applications. The proposed method is validated with both simulations and experiments in terms of the noise tolerance and the parameterization accuracy.

Multivariate identification of extruded PLA samples from the infrared spectrum

Polylactic acid (PLA) is a biodegradable thermoplastic polymer that is presented as a good alternative to petroleum-derived plastics. Some of the major drawbacks of this material are its lack of thermal stability and rapid degradation in large-scale production; thus, special care must be taken during processing. To improve their properties, a reactive extrusion with a multi-epoxy chain extender (SAmfE) has been performed at pilot plant scale. The induced topological modifications produce a mixture of several types of non-uniform structures. Conventional chromatographic (SEC—static light scattering) or spectroscopic (nuclear magnetic resonance) techniques usually fail in characterizing non-uniform structures. A method for the classification of modified PLA samples based on a multivariate treatment of the spectral data obtained by Fourier-transform infrared spectroscopy, jointly with the application of feature extraction and classification algorithms, was applied in this study. The results of this work show the potential of the methodology proposed to improve quality control during manufacturing.

Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies

Identification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of exploratory data analysis, such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical elements. The latter workflow involves analysis of 2D/3D produced maps, which can be mostly obtained by geostatistical algorithms. There are two challenging issues for such an analysis. The first one corresponds to handling the cross-correlation structures among the data, and the second one relates to the compositional nature of data. To tackle these problems, this paper investigates the application of Gaussian co-simulation for modeling the cross-correlated compositional data in order to recognize the multivariate geochemical anomalies in integration with fractal analysis. In this context, an innovative algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The compositional nature of data is addressed by additive log-ratio transformation of original data while the Gaussian co-simulation handles the reproduction of cross-correlation among the components. The co-simulated outputs are then taken into account for capturing different geochemical populations, showing different levels of backgrounds and anomalies. The algorithm is illustrated via a real case study located in Philippine wherever seven geochemical components are required to be considered. The accuracy of results is examined by statistical validation techniques, indicating the capability of the CoSS-N algorithm for multivariate identification of geochemical anomalies.

Computer analysis of the effect of the type of activating agent on the formation of the porous structure of activated carbon monoliths

The paper presents the results of an investigation into the effect of the type of activating agent on the formation of the microporous structure of activated carbon monoliths obtained by chemical activation involving calcium chloride, phosphoric(V) acid, and zinc chloride. The research into the microporous structure of the activated carbon monoliths subject to the analysis was based on the innovative method of porous structure analysis along with the unique fast procedure of multivariant identification of adsorptive systems. The analyses carried out with the use of the mentioned method were based on nitrogen, carbon dioxide, and methane adsorption isotherms. The research evidenced that a microporous structure analysis based on various adsorption isotherms and involving the method in question yields comprehensive and reliable information on the adsorptive properties of analysed materials, which is useful for technologists and designers alike.

Decomposition-based Gradient Estimation Algorithms for Multivariable Equation-error Systems

This paper concerns the parameter identification methods of multivariable equation-error systems. By means of the decomposition technique, the multivariable identification model is transformed into two sub-identification models and a decomposition-based stochastic gradient (D-SG) algorithm is presented for estimating the parameters of these two submodels. In order to further improve the convergence rate and the parameter estimation accuracy, we expand the innovation vectors to the innovation matrices and develop a decomposition-based multi-innovation stochastic gradient (D-MISG) algorithm. The simulation results confirm that the D-MISG algorithm can provide more accurate parameter estimates than the D-SG algorithm.

Identifying and treating outliers in finance

AbstractOutliers represent a fundamental challenge in the empirical finance research. We investigate whether the routine techniques used in finance research to identify and treat outliers are appropriate for the data structures we observe in practice. Specifically, we propose a multivariate identification strategy that can effectively detect outliers. We also introduce an estimator that minimizes the bias outliers caused in both cross‐sectional and panel regressions and provide outlier mitigation guidance. Using replications of four recently published studies in premier finance journals, we show how adjusting for multivariate outliers can lead to significantly different results.