Fast Sequential Algorithm Research Articles

A Fast Sequential Similarity Detection Algorithm for Multi-Source Image Matching

A Fast Sequential Similarity Detection Algorithm for Multi-Source Image Matching

Efficient Sequential and Parallel Prime Sieve Algorithms

Generating prime numbers less than or equal to an integer number m plays an important role in many asymmetric key cryptosystems. Recently, a new sequential prime sieve algorithm was proposed based on set theory. The main drawback of this algorithm is that the running time and storage are high when the size of m is large. This paper introduces three new algorithms for a prime sieve based on two approaches. The first approach develops a fast sequential prime sieve algorithm based on set theory and some structural improvements to the recent prime sieve algorithm. The second approach introduces two new parallel algorithms in the shared memory parallel model based on static and dynamic strategies. The analysis of the experimental studies shows the following results. (1) The proposed sequential algorithm outperforms the recent prime sieve algorithm in terms of running time by 98% and memory consumption by 80%, on average. (2) The two proposed parallel algorithms outperform the proposed sequential algorithm by 72% and 67%, respectively, on average. (3) The maximum speedups achieved by the dynamic and static parallel algorithms using 16 threads are 7 and 4.5, respectively. As a result, the proposed algorithms are more effective than the recent algorithm in terms of running time, storage and scalability in generating primes.

A Grey Wolf Optimized 15-Level Inverter Design with Confined Switching Components

Multilevel inverters are a new class of dc-ac converters designed for high-power medium voltage and power applications as they work at high switching frequencies and in renewable applications by avoiding stresses like dv/dt and has low harmonic distortion in their output voltage. In variable speed drives and power generation systems, the use of multilevel inverters is obligatory. To estimate the switching positions in inverter configuration with low harmonic distortion value, a fast sequential optimization algorithm has been established. For harmonic reduction in multilevel inverter design, a hybrid optimization technique combining Firefly and the Genetic algorithm was used. In several real-time systems and for solving complex engineering problems, optimization approaches are gaining popularity. Based on Grey Wolf Optimization (GWO), this research paper proposes a novel multilevel inverter architecture. The GWO algorithm is based on the natural leadership hierarchy and hunting mechanism of grey wolves (Canis lupus). The Grey Wolf Optimizer (GWO) algorithm is used to find the best switching angles for a cascaded multilevel inverter in order to eliminate some high order harmonics while sustaining the desired fundamental voltage. The proposed inverter has 15 stages, and the circuit’s unique feature includes a limited switching device. This proposed method comprises, MOSFET-based switches well as three DC sources in the main circuit. The switching parameters of the inverter topology are tuned using GWO in this methodology. The THD value of the proposed system is reduced to 6.629% compared to that of Multilevel Inverter using Genetic Algorithm, standalone power supply, Firefly assisted Genetic Algorithm, hybrid APSO algorithm, DC voltage regulation and FPGA. A few simulation studies have been included in this paper to affirm the capability of the hybrid topologies with various condition parameters, dynamic changes, and modulation indexes.

A fast sequential algorithm for the matrix chain ordering problem

SummaryThis article presents a fast sequential algorithm for the matrix chain ordering problem. Our solution is based on Yao's sequential algorithm that solves this problem in time by reducing the total number of distinct subproblems to be performed. We solve them fastly by avoiding some unnecessary computations. Our strategy consists in organizing the evaluation of the subproblems according to their dependencies instead of their precedence order as in the previous solutions. In many cases, our solution runs in time. An experimental study is conducted to benchmark the performance of our algorithm by measuring the average of the results obtained on five random data sets. This shows that our algorithm is 18.93 faster than Yao's sequential algorithm and 5.07 faster than the previous best CGM‐based parallel solutions on 32 processors.

A Fast Sequential MLE Algorithm for Jet Engine Classification With Radar

Radar signals scattered by an operating jet engine contain a unique signature that can be used to classify the type of engine, and therefore the aircraft. We present a new algorithm based on the maximum likelihood estimation (MLE) principle that can identify the number of blades of the rotors in the jet engine from the returned radar signal. Unlike the conventional algorithms, the proposed algorithm avoids the estimation/inspection of the autocorrelation of radar signal and utilizes the property of jet engine modulation (JEM) harmonics together with the successive interference cancellation (SIC) technique in the multi-dimensional search, which saves about 66.7% computing time compared to the autocorrelation based algorithm (specifically, the empirical mode decomposition (EMD) based algorithm). The proposed algorithm is assessed with synthetic data as well as real radar data using a mock-up jet engine. Specifically, by evaluating the mean-squared-errors and Cramer-Rao lower bound, we verify that the proposed SIC algorithm performs better than previously known jet engine-classification algorithms.

Brewery wastewater treatment plant key-component estimation using moving-window recurrent neural networks

This work proposes an experimental validation of software sensors for advanced on-line anaerobic digester process monitoring. The considered strategy is based on cheap available measurements (conductivity, temperature, pH, redox potential, etc) to reconstruct key component trajectories such as volatile fatty acid, carbonate and alkalinity concentrations, as well as biogas composition (methane, carbon dioxide, etc). The proposed solution considers a radial basis function artificial neural network (RBF -ANN) structure, using data processing (principal component analysis) and an efficient and fast sequential learning algorithm. In order to better reproduce unknown and complex process dynamics, the combination of a moving-window technique with a simple Jordan recurrent ANN structure (MW - RBF - RNN) is proposed. Comparative results based on real industrial data illustrate the estimation improvements provided by the MW - RBF - RNN with respect to the classical RBF - ANN structure.

Adjoint-Based Multi-Point and Multi-Objective Optimization of a Turbocharger Radial Turbine

This paper presents a gradient-based design optimization of a turbocharger radial turbine for automotive applications. The aim is to improve both the total-to-static efficiency and the moment of inertia of the turbine wheel. The search for the optimal designs is accomplished by a high-fidelity adjoint-based optimization framework using a fast sequential quadratic programming algorithm. The proposed method is able to produce improved Pareto-optimal designs, which are trade-offs between the two competing objectives, in only a few iterations. This is realized by redesigning the blade shape and the meridional flow channel for the respective target while satisfying imposed aerodynamic constraints. Furthermore, a comparative study with an evolutionary algorithm suggests that the gradient-based method has found the global Pareto front at a computational cost which is about one order of magnitude lower.

An improved first order local regularization method for ill-posed Volterra equations

In this paper, we present a first order local regularization method for solving ill-posed Volterra equations with ν-smoothing kernels, and establish stability and convergence of the method for all values of ν∈N. The method is an improvement of one whose numerical performance is shown to erode at ν=4 and whose convergence theory is uncertain once ν>4. We describe numerical implementation of the fast sequential algorithm associated with the method and provide a new scheme to approximate the initial condition. Numerical examples illustrate our theoretical results, particularly the method's stability in the case ν=4 and higher.

Redistribution of local fracture aperture and flow patterns by acidizing

Statistical and spatial distributions of apertures may be changed due to chemical dissolution. The chemical dissolution in rocks may result from the passage of acidic water through fractures or acid fracturing treatment. In this study, an acid injection cell was developed to simulate acidizing stimulation into fractures and investigate fractures surfaces etching and aperture evolution due to acidizing. Results showed that initial hydraulic and mechanical apertures and permeability (under zero stress) were increased due to fracture acidizing and a varying range of 2%–12% contact area was observed. Apertures before and after fracture acidizing followed a log-normal distribution and all of statistical moments were increased by acid injection while the calculated coefficient of variance in both arithmetic and logarithmic scales was decreased after acidizing. Correlation length increased in the direction of acid flow (x-direction) after acidizing. Results of fluid flow modeling using a developed finite element code showed a channeling path in the x-direction. Furthermore, using fast sequential simulation algorithm, apertures with various spatial correlation length in x and y directions were generated. Numerical modeling results of these generated aperture patterns showed that flow channelizing was increased by increasing of correlation length in the x-direction. Fluid flow rate was increased with increasing of correlation length in x-direction, while it decreased with increasing correlation length in y-direction.

Newtonized Orthogonal Matching Pursuit: Frequency Estimation Over the Continuum

We propose a fast sequential algorithm for the fundamental problem of estimating frequencies and amplitudes of a noisy mixture of sinusoids. The algorithm is a natural generalization of Orthogonal Matching Pursuit (OMP) to the continuum using Newton refinements, and hence is termed Newtonized OMP (NOMP). Each iteration consists of two phases: detection of a new sinusoid, and sequential Newton refinements of the parameters of already detected sinusoids. The refinements play a critical role in two ways: (1) sidestepping the potential basis mismatch from discretizing a continuous parameter space, (2) providing feedback for locally refining parameters estimated in previous iterations. We characterize convergence, and provide a Constant False Alarm Rate (CFAR) based termination criterion. By benchmarking against the Cramer Rao Bound, we show that NOMP achieves near-optimal performance under a variety of conditions. We compare the performance of NOMP with classical algorithms such as MUSIC and more recent Atomic norm Soft Thresholding (AST) and Lasso algorithms, both in terms of frequency estimation accuracy and run time.

Real-time fundamental current extraction under interharmonics in power system based on high resolution harmonics tracking and fast sequential least square

ObjectiveWe present a real-time fundamental current detection method under interharmonics and harmonics in power system based on high-resolution harmonics tracking algorithm (HRHTA) and fast sequential least square algorithm (FSLSA) for compensating interharmonics and harmonics simultaneously. MethodsThe fundamental frequency of voltage is estimated accurately based on HRHTA firstly; then, the fundamental component of the corresponding current is extracted based on FSLSA. Besides, the stable computing method and the computation-parameter selection rules are proposed for HRHTA. ResultsThe simulation results on the field interharmonics data show that the fundamental current can be extracted based on proposed method fast and accurately. ConclusionsThere is hard algorithm stability and high frequency resolution on the HRHTA in fundamental frequency real time detecting of voltage with time varied interharmonics and noise. The proposed the computation selection methods and hard limit in fundamental frequency detection has been test valid. Based FSLSA to extract fundamental component the compensation energy is least in theory.

A novel feature selection algorithm for high-dimensional condition monitoring data

The technique of machinery condition monitoring has been greatly enhanced over recent years with the application of many effective classifiers. However, these classification methods suffer from the 'curse of dimensionality' when applied to high-dimensional condition monitoring data. Actually, many classification algorithms are simply intractable when the number of features in the data is sufficiently large. In order to solve the problem, engineers have to resort to complicated feature extraction methods and other statistical theories to reduce the data dimensionality. However, features extracted using these methods lose their original engineering meaning and become obscure for engineers. In this study, a novel feature selection algorithm is presented to help to identify machinery condition quickly, based only on frequency spectrum data and without considering any complicated feature extraction methods. This brings many significant benefits: it can not only help engineers out of difficulties with complicated feature extraction methods, but it also gives a clear insight into the real condition of machinery without any loss of engineering meaning from the features used. The algorithm forms a functional extension of a relevance vector machine to feature selection, with a fast step-variable sequential backward search algorithm to search relevant scale parameters within a kernel function. Two case studies on different levels of condition monitoring are conducted to demonstrate the potential of applying the algorithm to high-dimensional engineering data.

Particle Learning of Gaussian Process Models for Sequential Design and Optimization

We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a fast sequential design algorithm for these models relative to the established MCMC alternative. The latter is less ideal for sequential design since it must be restarted and iterated to convergence with the inclusion of each new design point. We illustrate some attractive ensemble aspects of our SMC approach, and show how active learning heuristics may be implemented via particles to optimize a noisy function or to explore classification boundaries online. Supplemental material, including an R package, is available online.

A fast interactive sequential pattern mining algorithm

In order to reduce the computational and spatial complexity in rerunning algorithm of sequential patterns query, this paper proposes sequential patterns based and projection database based algorithm for fast interactive sequential patterns mining algorithm (FISP), in which the number of frequent items of the projection databases constructed by the correct mining which based on the previously mined sequences has been reduced. Furthermore, the algorithm's iterative running times are reduced greatly by, using global-threshold. The results of experiments testify that FISP outperforms PrefixSpan in interactive mining.

Sequential EM learning for subspace analysis

Subspace analysis is one of popular multivariate data analysis methods, which has been widely used in pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper, we present a fast sequential algorithm which behaves like expectation maximization (EM), for subspace analysis or tracking. In addition we also present a slight modification of the subspace algorithm by employing a rectifier, that is quite useful in handling nonnegative data (for example, images), which leads to rectified subspace analysis. The useful behavior of our proposed algorithms are confirmed through numerical experimental results with toy data and dynamic PET images.

Prediction of Reservoir Variables Based on Seismic Data and Well Observations

Offshore petroleum reservoirs are usually explored by acquisition of vast amounts of seismic reflection data and observations along a small number of wells drilled through the reservoirs. The seismic data are indirect measurements of reservoir characteristics and have good spatial coverage but low precision. The well observations are sparse but precise. Information from these sources of data are integrated to characterize the reservoir variables of interest—reservoir porosity in the current study. Critical parameters in the acquisition procedure are unknown and are simultaneously estimated. This process, termed seismic inversion, constitutes a spatial, multivariate, ill-posed inverse problem. The problem is cast in a Bayesian framework with a focus on sampling from the posterior model. High dimensionality, nonlinear components, and unknown parameters in the likelihood model, along with a complex design of the conditioning data, complicate the problem. A fast sequential sampling algorithm containing several parts subject to analytical evaluation is developed under relatively weak, realistic assumptions. Samples of spatial reservoir porosity characteristics can be used to predict and assess uncertainty in the petroleum volume contained in the reservoir. Moreover, porosity is highly correlated with permeability, which represents the fluid flow characteristics. Porosity and permeability are the critical factors in determining and optimizing the future production from petroleum reservoirs. The study is based on data from the Troll field in the North Sea.

TWO ALGORITHMS FOR COMPUTING THE EUCLIDEAN DISTANCE TRANSFORM

Given an n × n binary image of white and black pixels, we present two optimal algorithms for computing the distance transform and the nearest feature transform using the Euclidean metric. The first algorithm is a fast sequential algorithm that runs in linear time in the input size. The second is a parallel algorithm that runs in O(n2/p) time on a linear array of p processors, p, 1 ≤ p ≤ n.

A fast sequential source separation algorithm for digital cochannel signals

We propose a fast sequential-estimation algorithm for separating multiple cochannel digital signals using an antenna array with unknown array response. We present simulation results comparing speed and bit error rate (BER) performance with an existing fast joint-estimation algorithm, the iterative least squares with projection (ILSP) algorithm. The new algorithm proves to be faster with only a modest degradation in BER. In addition, convergence speed is very robust with respect to additive noise.

A Parallel Euclidean Distance Transformation Algorithm

We present a parallel algorithm for the Euclidean distance transformation (EDT). It is a “divide-and-conquer” algorithm based on a fast sequential algorithm for the signed EDT (SEDT). The combining step that follows the local partial calculation of the SEDT can be done efficiently after reformulating the SEDT problem as the partial calculation of a Voronoi diagram. This leads to an algorithm with two local calculation steps with a computational complexity proportional to the number of pixels of the subregions and a global calculation step with complexity proportional to the image perimeter. This article contains a description of the algorithm, a complexity analysis, a discussion on load imbalance, and timings obtained on an iPSC/2.

A fast sequential algorithm for pixel grey level ranking in adaptive rank filters applied to digital images

A fast sequential algorithm is presented for ranking the pixel grey levels which occur in the window of a digital filter as it is passed over a two-dimensional image. The algorithm is based on continually updating the frequency distribution of the grey levels in the window and ranking them in a one-dimensional array. The proposed algorithm is applied to images containing 256 grey levels and is faster than conventional sorting methods when more than a single ranked parameter is required.