Non-iterative Manner Research Articles

Artificial neural network model for predicting drill cuttings settling velocity

The traditional method of using the coefficient of drag – Reynolds number relationship to predict cuttings settling velocity involves an implicit procedure that requires repeated, time-consuming and tedious iterations using Newtonian or mostly non-Newtonian correlations. Usually, these correlations are limited to certain fluid flow regimes. Besides, most of the explicit and direct cuttings settling velocity models that exist are based on the assumption that the cuttings are spherical particles. However, in the field, the cuttings are a mixture of various shapes and are hardly spherical, hence these models when applied to field conditions come off with huge errors. The objective of this work was to use a nature-inspired algorithm (artificial neural network - ANN) to develop a model for estimating cuttings settling velocity that would be robust and useful in the field that would take into account the shape of the cuttings. The data used for this work was obtained from research experiments in the literature. The model was then evaluated using four performance metrics namely: mean squared error (MSE), root mean square error (RMSE), sum of squares error (SSE) and goodness of fit (R2). It was found that the model's predictions obtained in this work agreed with experimental evidence. Furthermore, the developed model possesses the capacity to generalize across new input datasets and can be applied to particles of any shape, hence, defining the novelty of this research and bridging the gap between theory and practice. When compared with state-of-the-art models, the developed models show a high degree of robustness, as the ANN model performed reasonably well with an MSE of 7.5 × 10−4, an R2 of 0.978, RMSE of 0.0274 and SSE of 0.25. To generalize the results across new input datasets, the developed model was cross-validated with new data that was not part of the training process. It was found that the ANN model had an MSE value 0.00807, RMSE of 0.0898, MAE of 0.065, SSE of 2.74 and MAPE of 0.675%. To ensure the replicability of the ANN model, the weights and biases for the inputs, hidden and output layers are presented in this work unlike other artificial intelligence-based models in the literature. The range of application for the developed ANN model is 0.0001 < Particle Reynolds's number <100 and 0.471 < cuttings sphericity <1. With the model developed in this work, the cuttings settling velocity can be predicted with minimal errors in a quick, less cumbersome, non-iterative manner and is not limited by cuttings shapes' factor and fluid flow regimes.

Ion Pairing in HCl-Water Clusters: From Electronic Structure Investigations to Multiconfigurational Force-Field Development.

In the bulk, condensed-phase HCl exists as a dissociated Cl- ion and a proton that is delocalized over solvating water molecules. However, in the gas phase, HCl is covalent, and even on the introduction of hydrating water molecules, the HCl covalent state dominates small clusters and is relevant at larger clusters including 21 water molecules. Electronic structure calculations (at the MP2 level) and ab initio metadynamics simulations (at the DFT level) have been carried out on HCl-(H2O)n clusters with n = 2-22 to investigate distinct solvation environments in clusters from covalent HCl structure, to contact ion pairs and solvent-separated ion pairs. The data were further used to train and validate a multiconfigurational force-field for HCl-water clusters that incorporates covalent HCl states into the MS-EVB3.2 formalism. Additionally, the many-body interaction of the Cl- ion with water and the excess proton was modeled by the introduction of two geometric three-body terms that incorporates the dominant many-body interaction in an efficient noniterative manner.

Phase retrieval with extended field of view based on continuous phase modulation

In order to address two main obstacles that affect the practical application of coherent modulation imaging (CMI)i.e., the low signal-to-noise ratio (SNR) and limited field of view (FOV), a new algorithm providing extended FOV based on CMI is proposed. A weak scattering modulator was used instead of a binary random phase modulator with strong scattering ability, to improve the final resolution of CMI combined with probe scanning. An unlimited FOV was achieved in a noniterative manner, resulting in a superior SNR compared with ptychography. Compared with the original CMI with a binary random phase modulator, the errors decreased obviously when continuous phase plate was utilized based on simulation and experimental results. Moreover, the random speckles that are associated with coherent diffractive imaging could be effectively eliminated. The proposed algorithm facilitates single-shot or extended FOV phase imaging with a high SNR and high resolution.

Modal decomposition for few mode fibers using the fractional Fourier system.

Modal decomposition (MD) has become an indispensable diagnostic tool for optical fibers. A novel MD method using the fractional Fourier system is developed in this paper. Firstly, the existing phase retrieval (PR) algorithm based on the the fractional Fourier transform (FrFT) power spectra is extended to account for the effect of optical vortex. The extended capability can then be employed to reconstruct the phase of modal field to high fidelity in a non-iterative and non-interferometric manner. Combining the reconstructed phase with the measured near-field (NF) intensity, the modal field could be obtained and based upon which the complete MD (involving modal weights and phases) is performed. The validity and reliability of the method are demonstrated through several numerical examples including the noisy signals with different signal-to-noise ratio (SNR) levels.

Pilot Allocation and Computationally Efficient Non-Iterative Estimation of Phase Noise in OFDM

This letter proposes a pilot subcarrier allocation approach for orthogonal frequency division multiplexing systems, which enables construction of symbol-wise phase-noise (PN) estimates with high efficiency and low overhead in a non-iterative manner. The complexity and performance of the overall PN suppression algorithm together with the proposed pilot allocation approach are evaluated in 5G single-user-multiple-input multiple-output (MIMO) and multi-user-MIMO radio links at 28 GHz carrier frequency, showing clear complexity-performance benefits against a state-of-the-art reference algorithm.

Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise

The uniform white noise assumption is one of the basic assumptions in most of the existing directional-of-arrival (DOA) estimation methods. In many applications, however, the non-uniform white noise model is more adequate. Then the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of non-uniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.

Two-degree-of-freedom multi-input multi-output proportional–integral–derivative control design: Application to quadruple-tank system

This article is concerned with designing a 2-degree-of-freedom multi-input multi-output proportional–integral–derivative controller to ensure linear quadratic regulator performance and H∞ performance using a non-iterative linear matrix inequality–based method. To design the controller, first, a relation between the state feedback gain and proportional–integral–derivative gain is obtained. As the gains of proportional–integral–derivative controller cannot, in general, be found out from this relation for arbitrary stabilizing state feedback gain, a suitable form of the matrices involved in linear matrix inequality–based state feedback design is then chosen to obtain the proportional–integral–derivative gains directly. The special structure of the above matrices allows one to design proportional–integral–derivative controller in non-iterative manner. As a result, multi-objective performances, such as linear quadratic regulator and H∞, can be achieved simultaneously without increasing the computational burden much. To enhance the reference-input-to-output characteristics, a feedforward gain is also introduced and designed to minimize certain closed-loop H∞ performance. The proposed control design method is applied for multi-input multi-output proportional–integral compensation of a laboratory-based quadruple-tank process. The performance of the compensation is studied through extensive simulations and experiments.

Exploiting algebraic structure in global optimization and the Belgian chocolate problem

The Belgian chocolate problem involves maximizing a parameter $$\delta $$ over a non-convex region of polynomials. In this paper we detail a global optimization method for this problem that outperforms previous such methods by exploiting underlying algebraic structure. Previous work has focused on iterative methods that, due to the complicated non-convex feasible region, may require many iterations or result in non-optimal $$\delta $$ . By contrast, our method locates the largest known value of $$\delta $$ in a non-iterative manner. We do this by using the algebraic structure to go directly to large limiting values, reducing the problem to a simpler combinatorial optimization problem. While these limiting values are not necessarily feasible, we give an explicit algorithm for arbitrarily approximating them by feasible $$\delta $$ . Using this approach, we find the largest known value of $$\delta $$ to date, $$\delta = 0.9808348$$ . We also demonstrate that in low degree settings, our method recovers previously known upper bounds on $$\delta $$ and that prior methods converge towards the $$\delta $$ we find.

PQ decoupled 3-phase numerical observability analysis and critical data identification for distribution systems

Summary Emerging active distribution systems are operated under more complicated and uncertain conditions. Because of frequent topology changes or temporary malfunctions of data acquisition, the network may lose observability and in turn may lead to the failure of distribution system state estimation. Without distribution system state estimation, the distribution system operator may not be able to make secure decisions, and the system may face with risk of serious damages. In this paper, a new real/reactive (PQ) decoupled 3-phase numerical observability analysis method and a new critical data identification method for distribution systems are proposed. The methods can efficiently identify all unobservable branches and critical data in a non-iterative manner. In addition to the 3-phase decoupling, the normal equation is also PQ decoupled, and the computation burden is greatly reduced. Furthermore, all entries in the Jacobian matrix are non-negative integers and the proposed methods present good numerical performance. Strict theoretical proofs on the performance of the proposed methods are provided and numerical results on different scales of test systems illustrate their validities.

Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection

Minimum Entropy Deconvolution (MED) has been applied successfully to rotating machine fault detection from vibration data, however this method has limitations. A convolution adjustment to the MED definition and solution is proposed in this paper to address the discontinuity at the start of the signal – in some cases causing spurious impulses to be erroneously deconvolved. A problem with the MED solution is that it is an iterative selection process, and will not necessarily design an optimal filter for the posed problem. Additionally, the problem goal in MED prefers to deconvolve a single-impulse, while in rotating machine faults we expect one impulse-like vibration source per rotational period of the faulty element. Maximum Correlated Kurtosis Deconvolution was proposed to address some of these problems, and although it solves the target goal of multiple periodic impulses, it is still an iterative non-optimal solution to the posed problem and only solves for a limited set of impulses in a row. Ideally, the problem goal should target an impulse train as the output goal, and should directly solve for the optimal filter in a non-iterative manner. To meet these goals, we propose a non-iterative deconvolution approach called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA proposes a deconvolution problem with an infinite impulse train as the goal and the optimal filter solution can be solved for directly. From experimental data on a gearbox with and without a gear tooth chip, we show that MOMEDA and its deconvolution spectrums according to the period between the impulses can be used to detect faults and study the health of rotating machine elements effectively.

Still image/video frame lossy compression providing a desired visual quality

The problem of how to automatically provide a desired (required) visual quality in lossy compression of still images and video frames is considered in this paper. The quality can be measured based on different conventional and visual quality metrics. In this paper, we mainly employ human visual system (HVS) based metrics PSNR-HVS-M and MSSIM since both of them take into account several important peculiarities of HVS. To provide a desired visual quality with high accuracy, iterative image compression procedures are proposed and analyzed. An experimental study is performed for a large number of grayscale test images. We demonstrate that there exist several coders for which the number of iterations can be essentially decreased using a reasonable selection of the starting value and the variation interval for the parameter controlling compression (PCC). PCC values attained at the end of the iterative procedure may heavily depend upon the coder used and the complexity of the image. Similarly, the compression ratio also considerably depends on the above factors. We show that for some modern coders that take HVS into consideration it is possible to give practical recommendations on setting a fixed PCC to provide a desired visual quality in a non-iterative manner. The case when original images are corrupted by visible noise is also briefly studied.

Power system state estimation using a direct non-iterative method

Abstract This paper describes a new method for state estimation of a non-linear AC power system in a non-iterative manner. This method is based on the Kipnis–Shamir relinearization technique that is used to solve over-defined sets of polynomial equations. The technique transforms the equations to a higher dimensional linear space which allows the states to be solved in a non-iterative manner. Given accurate measurements, this new state estimation method provides the same results as traditional iterative state estimation methods, and the proposed method does not require an initial guess of system states nor does it have issues with convergence.

Precoder selection scheme based on message-passing approach: a practical perspective

This work addresses a distributed technique to precoding matrices coordination in a multi-cell network. We propose an iterative method based on a message-passing algorithm in factor graphs. Finite precoder codebooks for different MIMO setups are adopted for beam forming purposes. Conversely, practical cellular networks usually solve the precoder selection problem in a non-iterative manner based on the best response method. Nevertheless, this method may not provide a near-optimal solution. For more realistic results, the wireless channel is modeled based on measured data that comprises spatial correlation effects. Evaluations on the potential of such an approach are provided and its performance is compared with the selfish/greedy approach. The capability of reaching the globally optimal solution is evaluated, as well as its performance per iteration. Simulation results for the precoder selection example are presented and discussed, which show that the graph-based technique generally obtains gain in system capacity over both the non-iterative and selfish approaches. Besides, the proposed method usually reaches the global optima in an efficient manner in terms of signaling load.

Parameterization of Three-Phase Electric Machine Models for EMI Simulation

A systematic and practical method to parameterize three-phase electric machine models for electromagnetic interference (EMI) simulation is presented. The proposed behavioral model consists of multiple sections of linear RLC circuits and is intended for time-domain simulation with inverters and other power electronic circuits found in typical motor drive systems. The proposed parameterization method uses a differential-mode (DM) and a common-mode (CM) impedance measurement of the machine and takes advantage of the separation among different parallel and series resonant frequencies of each impedance to determine the parameters of each stage in a noniterative manner. The proposed method can also be applied to model three-phase cables and transformers.

Observability analysis and restoration for systems with conventional and phasor measurements

SUMMARY This paper presents an efficient and fast method, using topological and numerical procedures, for observability analysis in state estimation with phasor measurement units as well as conventional (SCADA) measurements. Power and current flow measurements are used to build topologically flow islands that, in turn, are used to construct a reduced network and an associated gain matrix. To build this reduced order gain matrix, boundary power injections, and voltage phasors at flow islands are considered. Observability checking is carried out analyzing the pivots encountered during the triangular factorization of the gain matrix, and the observable islands are identified in a noniterative manner, by performing back substitutions with its triangular factors. Additional measurements that will render the system barely observable are directly provided, using the triangular factors of a reduced Gram matrix associated with existing and candidate power injections and candidate phasor measurements at boundary buses of observable islands. The dimension of the gain and Gram matrix is equal to the number of flow islands and boundary measurements, respectively, which is relatively low even for very large systems, and that makes the proposed algorithms fast and computationally efficient. The IEEE 14 and 30-bus systems are used to illustrate the steps of the proposed algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model

Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted-living environments, and surveillance. In these scenarios, we may have to consider adding new motion classes (i.e., new types of human motions to be recognized), as well as new training data (e.g., for handling different type of subjects). Hence, both the accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this article, we propose a knowledge-based hybrid (KBH) method that can compute the probabilities for hidden Markov models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMM parameter in a noniterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as in reduced training time. Moreover, we show in additional experiments that the KBH method also outperforms the linear support vector machine (SVM).

Bilinear Power System State Estimation

This paper presents a three-stage state estimation methodology based on the sequential solution of two weighted least squares (WLS) linear problems with a nonlinear explicit transformation in between. This requires that appropriate sets of auxiliary variables be introduced so that the resulting measurement models become linear. Simulation results show that the proposed approach yields, in a non-iterative manner, virtually the same solution as that provided by the Gauss-Newton iterative scheme arising in the conventional WLS method.

Observability and criticality analysis in state estimation using integer-preserving Gaussian elimination

SUMMARY This paper presents an efficient numerical method for observability and criticality analysis in power system state estimation based on integer Gaussian elimination of integer coefficient matrices. Because all computations performed are exact, no round-off error, numerical instability, or zero identification problems occur. The observable islands are identified in a noniterative manner by performing back substitutions on the integer triangular factors of the gain matrix. The additional measurements for placement are provided by a direct method, using the integer triangular factors of the Gram matrix associated with a reduced size Jacobian matrix. The critical measurements are identified by checking for diagonal entries of the integer residual sensitivity matrix. Implementation of the proposed method needs little additional effort, because it requires minor modifications of sparse triangular factorization and forward/backward substitution algorithms common to state estimators. The IEEE 14-bus system is used to illustrate the steps of the proposed algorithms. Test results for a 10 343-bus system are provided to demonstrate the features of the proposed algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

Automatic registration between 3D intra-operative ultrasound and pre-operative CT images of the liver based on robust edge matching

The registration of a three-dimensional (3D) ultrasound (US) image with a computed tomography (CT) or magnetic resonance image is beneficial in various clinical applications such as diagnosis and image-guided intervention of the liver. However, conventional methods usually require a time-consuming and inconvenient manual process for pre-alignment, and the success of this process strongly depends on the proper selection of initial transformation parameters. In this paper, we present an automatic feature-based affine registration procedure of 3D intra-operative US and pre-operative CT images of the liver. In the registration procedure, we first segment vessel lumens and the liver surface from a 3D B-mode US image. We then automatically estimate an initial registration transformation by using the proposed edge matching algorithm. The algorithm finds the most likely correspondences between the vessel centerlines of both images in a non-iterative manner based on a modified Viterbi algorithm. Finally, the registration is iteratively refined on the basis of the global affine transformation by jointly using the vessel and liver surface information. The proposed registration algorithm is validated on synthesized datasets and 20 clinical datasets, through both qualitative and quantitative evaluations. Experimental results show that automatic registration can be successfully achieved between 3D B-mode US and CT images even with a large initial misalignment.

Observability Analysis Based on Echelon Form of a Reduced Dimensional Jacobian Matrix

This letter presents an efficient numerical method to determine the observable islands and additional measurements for placement, in an unobservable system. This is accomplished in a noniterative manner via the use of the Echelon form of a reduced order measurement Jacobian matrix, that is associated with a subset of the injection measurements and a subnetwork of the entire system. The proposed method is faster and easier to implement than existing approaches and gives more reliable results due to better conditioning of the processed matrices. The IEEE 14-bus system illustrates the steps of the proposed method.