Recursive Approach Research Articles

Quantification of High‐Temperature Transition Al2O3and Their Phase Transformations**

AbstractHigh‐temperature treatment of γ‐Al2O3can lead to a series of polymorphic transformations, including the formation of δ‐Al2O3and θ‐Al2O3. Quantification of the microstructure in the range where δ‐ and θ‐Al2O3are formed represents a formidable challenge, as both phases accommodate a high degree of structural disorder. In this work, we explore the use of an XRD recursive‐stacking formalism for the quantification of high‐temperature transition aluminas. We formulate the recursive‐stacking methodology for modelling of disorder in δ‐Al2O3and twinning in θ‐Al2O3and show that explicitly accounting for the disorder is necessary to reliably model the XRD patterns of high‐temperature transition alumina. We also use the recursive stacking approach to study phase transformation during high‐temperature (1050 °C) treatment. We show that the two different intergrowth modes of δ‐Al2O3have different transformation characteristics and that a significant portion of δ‐Al2O3is stabilized with θ‐Al2O3even after prolonged high‐temperature exposures.

Data-driven design of fault-tolerant control systems based on recursive stable image representation

Driven by the increasing needs in the process industry for designing fault-tolerant control systems based on process data and maintaining its performance during online operation, data-driven realization and design of feed-forward fault-tolerant control systems with embedded residual generation are studied. To this end, a parameterized and data-driven realization form of stable image representation in a feedback control loop is derived first. On this basis, a feed-forward controller design scheme is proposed for fault-tolerant purpose. It is followed by a recursive approach for online tuning the feed-forward controller in case of malfunctions, which is triggered by a fault detection scheme based on the so-called stable kernel representation. The effectiveness of the proposed fault-tolerant method is demonstrated by a case study on the laboratory three-tank system.

A multistage recursive approach in time- and frequency-domain for thermal analysis of thermochromic glazing and thermostatic control systems in buildings

The frequency-domain analysis is an effective approach to analyze the steady-periodic heat transfer in buildings. Auxiliary load in buildings can be calculated using the indoor temperature in a frequency-domain analysis. However, both auxiliary load and indoor temperature are usually unknown in building analysis, which makes computing the indoor temperature and auxiliary load with the presence of heating and cooling setpoints are challenging in a frequency-domain analysis. A similar problem applies to the calculation of the auxiliary loads of buildings with dynamic materials, such as thermochromic materials. This paper proposes and implements a recursive approach in time- and frequency-domain to calculate the indoor temperature in an office space with dual setpoint and thermochromic glazing systems with different critical transition temperatures and tinted-to-clear transmissivity ratios. This method allows for capturing the free-floating fluctuations of the indoor temperature within the deadband as well as determining the points-in-time when the indoor temperature equals heating or cooling setpoint temperatures. A multistage procedure is defined to account for the varying window transmittance. The proposed method accounts for the time-dependent response of a thermostat and thermochromic glazing within a finite number of adjustments without requiring to carry out time-domain temperature or load calculations. Results revealed reductions in the annual cooling energy use and increases in the annual heating energy consumption using thermochromic glazing systems with the total annual energy savings of up to 1.22 kWh/m2-yr for the τ-ratio of 0.65, 3.50 kWh/m2-yr for the τ-ratio of 0.30, and 5.22 kWh/m2-yr for the hypothetical case with zero τ-ratio.

RECURSIVE HIERARCHICAL CLUSTERING FOR HYPERSPECTRAL IMAGES

Abstract. Partition based clustering techniques are widely used in data mining and also to analyze hyperspectral images. Unsupervised clustering only depends on data, without any external knowledge. It creates a complete partition of the image with many classes. And so, sparse labeled samples may be used to label each cluster, and so simplify the supervised step. Each clustering algorithm has its own advantages, drawbacks (initialization, training complexity). We propose in this paper to use a recursive hierarchical clustering based on standard clustering strategies such as K-Means or Fuzzy-C-Means. The recursive hierarchical approach reduces the algorithm complexity, in order to process large amount of input pixels, and also to produce a clustering with a high number of clusters. Moreover, in hyperspectral images, a classical question is related to the high dimensionality and also to the distance that shall be used. Classical clustering algorithms usually use the Euclidean distance to compute distance between samples and centroids. We propose to implement the spectral angle distance instead and evaluate its performance. It better fits the pixel spectrums and is less sensitive to illumination change or spectrum variability inside a semantic class. Different scenes are processed with this method in order to demonstrate its potential.

Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis

The integration of large-scale wind farms and large-scale charging stations for electric vehicles (EVs) into electricity grids necessitates energy storage support for both technologies. Matching the variability of the energy generation of wind farms with the demand variability of the EVs could potentially minimize the size and need for expensive energy storage technologies required to stabilize the grid. This paper investigates the feasibility of using the wind as a direct energy source to power EV charging stations. An interval-based approach corresponding to the time slot taken for EV charging is introduced for wind energy conversion and analyzed using different constraints and criteria, including the wind speed averaging time interval, various turbines manufacturers, and standard high-resolution wind speed datasets. A quasi-continuous wind turbine’s output energy is performed using a piecewise recursive approach to measure the EV charging effectiveness. Wind turbine analysis using two years of wind speed data shows that the application of direct wind-to-EV is able to provide sufficient constant power to supply the large-scale charging stations. The results presented in this paper confirm that the potential of direct powering of EV charging stations by wind has merits and that research in this direction is worth pursuing.

Tracking Control Strategy Using Filter-Based Approximation for the Unknown Control Direction Problem of Uncertain Pure-Feedback Nonlinear Systems

A filter-based recursive tracker design approach is presented for the problem of unknown control directions of pure-feedback systems with completely unknown non-affine nonlinearities. In the controller design procedure, the first-order filters for error surfaces, a control input, and state variables are employed to design nonadaptive virtual and actual control laws independent of adaptive function approximators. In addition, for the unknown control direction problem, the filtering signals are incorporated with Nussbaum functions. Different from existing adaptive approximation-based control schemes in the presence of unknown control directions, the proposed approach does not require any adaptive technique regardless of completely unknown nonlinear functions. Therefore, a simplified tracking structure can be constructed. Using the Lyapunov stability analysis, it is shown that the tracking error is reduced within an adjustable neighborhood of the origin while ensuring all the closed-loop signals are bounded.

Learning Causal Structures Based on Divide and Conquer

This article addresses two important issues of causal inference in the high-dimensional situation. One is how to reduce redundant conditional independence (CI) tests, which heavily impact the efficiency and accuracy of existing constraint-based methods. Another is how to construct the true causal graph from a set of Markov equivalence classes returned by these methods. For the first issue, we design a recursive decomposition approach where the original data (a set of variables) are first decomposed into two small subsets, each of which is then recursively decomposed into two smaller subsets until none of these subsets can be decomposed further. Redundant CI tests can be reduced by inferring causalities from these subsets. The advantage of this decomposition scheme lies in two aspects: 1) it requires only low-order CI tests and 2) it does not violate d -separation. The complete causality can be reconstructed by merging all the partial results of the subsets. For the second issue, we employ regression-based CI tests to check CIs in linear non-Gaussian additive noise cases, which can identify more causal directions by [Formula: see text] (or [Formula: see text]). Consequently, causal direction learning is no longer limited by the number of returned V -structures and consistent propagation. Extensive experiments show that the proposed method can not only substantially reduce redundant CI tests but also effectively distinguish the equivalence classes.

ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches.

In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.

Removal of micro‐Doppler effect in ISAR imaging based on time recursive iterative adaptive approach

The signal separation method based on the short time Fourier transform (STFT) and the L-statistics can effectively eliminate the interference of the micro-Doppler effect on inverse synthetic aperture radar (ISAR) imaging. The insufficient frequency resolution of STFT will result in poor interference cancellation when the azimuth range between two adjacent scatterers is small. Then, a novel method for the elimination of micro-Doppler effect in ISAR imaging based on the time recursive iterative adaptive approach (TRIAA) is proposed here. This method uses the TRIAA technique to improve the frequency resolution compared with STFT, thereby enhancing the ability to process the complicated signal. The ISAR imaging results for the simulation model and raw data illustrate the effectiveness of the algorithm here.

Nonrecursive separation of risk and time preferences

Recursive utility disentangles preferences with respect to time and risk by recursively building up a value function of local increments. This involves certainty equivalents of indirect utility. Instead we disentangle preferences with respect to time and risk by building up a value function as a non-linear aggregation of certainty equivalents of direct utility of consumption. This entails time-consistency issues which are dealt with by looking for an equilibrium control and an equilibrium value function rather than a classical optimal control and a classical optimal value function. We characterize the solution in a general diffusive incomplete market model and find that, in certain special cases of utmost interest, the characterization coincides with what would arise from a recursive utility approach. But also importantly, in other cases, it does not: The two approaches are fundamentally different but match, exclusively but importantly, in the mathematically special case of homogeneity of the value function.

Application of Recursive Theory of Slow Viscoelastic Flow to the Hydrodynamics of Second-Order Fluid Flowing through a Uniformly Porous Circular Tube

Slow velocity fluid flow problems in small diameter channels have many important applications in science and industry. Many researchers have modeled the flow through renal tubule, hollow fiber dialyzer and flat plate dialyzer using Navier Stokes equations with suitable simplifying assumptions and boundary conditions. The aim of this article is to investigate the hydrodynamical aspects of steady, axisymmetric and slow flow of a general second-order Rivlin-Ericksen fluid in a porous-walled circular tube with constant wall permeability. The governing compatibility equation have been derived and solved analytically for the stream function by applying Langlois recursive approach for slow viscoelastic flows. Analytical expressions for velocity components, pressure, volume flow rate, fractional reabsorption, wall shear stress and stream function have been obtained correct to third order. The effects of wall Reynolds number and certain non-Newtonian parameters have been studied and presented graphically. The obtained analytical expressions are in agreement with the existing solutions in literature if non-Newtonian parameters approach to zero. The solutions obtained in this article may be considered as a generalization to the existing work. The results indicate that there is a significant dependence of the flow variables on the wall Reynolds number and non-Newtonian parameters.

Riemannian geometry for compound Gaussian distributions: Application to recursive change detection

A new Riemannian geometry for the zero-mean Compound Gaussian distribution with deterministic textures is proposed. In particular, the Fisher information metric (up to a factor) is obtained, along with corresponding geodesics and distance function. This new geometry is applied on a change detection problem on Multivariate Image Times Series: a recursive approach based on Riemannian optimization is developed. As shown on simulated data, it allows to reach optimal performance while being computationally more efficient.

Decentralized adaptive quantized feedback tracking of a class of uncertain interconnected lower-triangular nonlinear systems

A decentralized adaptive quantized feedback tracking problem is addressed for uncertain interconnected nonlinear systems in a strict-feedback form. All local state variables for each subsystem are quantized by a hysteresis quantizer. Different from the existing results in the literature, the primary contribution of this study is to establish an adaptive control design and stability analysis strategy using the local quantized state variables in the decentralized tracking field. A novel recursive design approach is presented to deal with unmatched interconnections and nonlinearities in the quantized feedback control framework, without any restrictions on virtual control laws. The total closed-loop stability is analyzed by deriving some technical lemmas on the boundedness of quantization error signals.

Reliability and capacity evaluation of multi-performance multi-state weighted K −-out-of-n systems

Systems in engineering usually not only have multiple states but also may be required to fulfill several functions, meaning the system has multiple performance variables in a specific state. In a multi-performance multi-state weighted K −-out-of-n system, the system consists of several multi-performance multi-state components for completing its different functions. As this system is more complicated than the general weighted k-out-of-n system, reliability evaluation of the system is time-consuming. A recursive approach is developed to evaluate the system reliability more efficiently, and comparison with the existing approach is carried out in various aspects. It is found the developed method is generally more efficient. To further understand the dynamic behavior of the multi-performance multi-state weighted K −-out-of-n system, two measures: capacity loss and residual capacity are introduced to investigate the system capacity evaluation over time. A method is provided for the computation of mean capacity loss and mean residual capacity of the system. A numerical example is presented to demonstrate the proposed method. The result shows the two measures can give an idea of how much capacity is lost and how much capacity is available, and provide effective information for system maintenance and optimization.

Financial intermediation and capital reallocation

To understand the link between financial intermediation activities and the real economy, we build a general equilibrium model in which agency frictions in the financial sector affect the efficiency of capital reallocation across firms and generate aggregate economic fluctuations. We develop a recursive policy iteration approach to fully characterize the nonlinear equilibrium dynamics and the off-steady-state crisis behavior. In our model, adverse shocks to agency frictions exacerbate capital misallocation and manifest themselves as variations in total factor productivity at the aggregate level. Our model endogenously generates countercyclical volatility in the aggregate time series and countercyclical dispersion in the marginal product of capital and asset returns in the cross-section.

Recursive density-based hierarchical clustering in gaussian distributed sensor network

Sensor networks are data-centric networks constrained with limited battery power and processing capabilities. One of the crucial challenges in sensor network is energy hole problem. In order to deal with the challenge, there exists several mechanisms, of which clustering is considered an energy-efficient solution. In general, clustering refers to the technique of grouping nodes on the basis of similarity in spatial arrangement. An appropriately clustered network helps in processing and aggregation of sensed data before routing the information to destined location. This paper proposes a soft computing based recursive approach for implementing density-based hierarchical clustering, for Gaussian distributed sensor network. Our proposed probabilistic approach creates hierarchical clusters recursively, which not only addresses the problem of energy hole, but also reduces transmission delay, thereby maintaining data freshness.

Incomplete block‐matrix factorization of M‐matrices using two‐step iterative method for matrix inversion and preconditioning

Using the general method of Owe Axelsson given in 1986 for incomplete factorization of M‐matrices in block‐matrix form, we give a recursive approach to construct incomplete block‐matrix factorization of M‐matrices by proposing a two‐step iterative method for the approximation of the inverse of diagonal pivoting block matrices at each stage of the recursion. For various predescribed tolerances in the accuracy of the approximation of the inverses, the obtained incomplete block‐matrix factorizations are used to precondition the iterative methods as one‐step stationary iterative (OSSI) method and biconjugate gradient stabilized method (BI‐CGSTAB). Certain applications are conducted on M‐matrices occurring from the discretization of two Dirichlet boundary value problems of Laplace's equation on a rectangle using finite difference method. Numerical results justify that the given incomplete block‐matrix factorization of M‐matrices using the two‐step iterative method to approximate the inverse of diagonal pivoting block matrices at each stage of the recursion give robust preconditioners. The obtained results are presented through tables and figures.

Adaptive Robust Tracker Design for Nonlinear Sandwich Systems Subject to Saturation Nonlinearities

SUMMARYThis paper addresses the tracking problem for uncertain nonlinear sandwich systems that consist of two nonlinear subsystems and saturation nonlinearity, which is sandwiched between the subsystems. The considered sandwich system is also subject to a nonsymmetric input saturation constraint. Due to the nonsmooth characteristics of sandwiched saturation nonlinearity and also the input saturation function, the design procedure deals with hard challenges. To overcome these difficulties, a recursive approach is suggested that consists of two phases. For the implementation of the proposed approach, a tracking problem is solved in each phase. In the first phase, the second subsystem with sandwiched saturation nonlinearity is considered and the output tracking problem of the desired time-varying reference signal is solved using backstepping method. The outcome of the first phase is the desired reference signal that should be tracked by the first subsystem in the next phase. In the second phase, the robust control input is designed for the first subsystem by employing adaptive sliding mode technique such that, despite the nonsymmetric input saturation constraint, model uncertainty and external disturbances, the output of the first subsystem follows the desired signal that is obtained in the previous phase. The simulation results for a mechanical sandwich system are illustrated to verify the effectiveness of the proposed control method.

An Improved Recursive Total Least Squares Estimation of Capacity for Electric Vehicle Lithium-Iron Phosphate Batteries

A battery’s capacity is an important indicator of its state of health and determines the maximum cruising range of electric vehicles. It is also a crucial piece of information for helping improve state of charge (SOC) estimation, health prognosis, and other related tasks in the battery management system (BMS). In this paper, we propose an improved recursive total least squares approach to online capacity estimation, which is based on the constrained Rayleigh quotient in terms of battery capacity. This approach accounts for errors in both the SOC and accumulated current measurements not traditionally considered in the battery capacity model to give an unbiased estimation. Moreover, the forgetting factor, updated by minimizing the Rayleigh quotient of the capacity estimation model, is applied to track the changes in the model and get a more precise estimation of the capacity. Finally, the performance of the proposed algorithm is validated via simulation and experimental studies on lithium-iron phosphate batteries. The estimation results show that the proposed algorithm improves capacity estimation accuracy.

Identification of distinct risk subsets for under five mortality in India using CART model: an evidence from NFHS-4

Background The objective of this study was to find the distinct risk subsets or clusters identified by the combination of factors and important factors to classify under five mortality (U5M) in high focused Indian states. Methods Using population-based cross-sectional data from the National Family Health Survey (NFHS, 2015-2016) on 1, 40, 427 live births of five years preceding the survey occurred to 99,205 women of high focused Indian states with U5M rate above the national level, a recursive partitioning approach based two classification tree models, one without considering missing values and other with missing together approach, were fitted using binary outcome of U5M and independent factors comprising of socioeconomic, demographic, maternal and biological, nutritional and environmental factors. Results There were nine and sixteen sub-groups in model-1 and model-2, respectively. In model-1, breastfeeding = no & birth in past 5 years = (2, 3+ births) and in model-2, breastfeeding = no & birth weight = (<2.5kg, not known) & birth in past 5 years = (2, 3 or more births) were found to be maximum mortality risk sub-groups. In terms of variable importance to predict U5M, model-1 identified birth in past 5 years, breastfeeding, birth order, wealth index, mother‘s age at birth. Model-2 additionally identified delivery complications, birth weight, state, sanitation facility, birth interval, caste, education. Overall correct classification rate was higher for model-1 (66%) than model-2 (64%). Conclusions The main observed risk cluster was combination of two factors like breastfeeding and number of births in past 5 years, which for most people are easily modifiable with appropriate strategies and policies. Finally, to combat U5M in high focused states, identifying risk subsets or clusters is important for targeting and intervening purposes, as the intensity and type of policies and programs may differ according to clusters. This method is suitable to identify complex natural interactions between predictors, important variables and hypothesis generation to inform policy maker on intervention strategies, which may be difficult or impossible to uncover using traditional multivariable techniques.