Recursive Technique Research Articles

An analytical wave solution for the vibrational response and energy of an axially translating string in any propagation cycle

An axially traveling string system, which is a kind of traveling material, attracts considerable attention owing to its broad applications. In this paper, an analytical wave solution for the vibration and energy of an axially traveling string with fixed and viscous damper (dashpot) boundaries in any propagation cycle is considered. Firstly, a novel recursive and simplified technique is proposed to expand the analytical solution for a traveling string to any propagation cycle, which was limited to only one propagation cycle due to complexity in previous work. As a kind of analytical solution, the traveling wave method has more accuracy and efficiency compared to numerical methods. Secondly, different from the previous result, the modified Hamilton’s principle is applied to the derivation of the dashpot boundary condition for the mass changing of the traveling string. Following the pipeline hydrodynamics theory, the energy gradient for the ‘control volume’ and the ‘system’ of traveling string are accurately obtained, respectively. Thirdly, from the point of view of vibration suppression, the optimal damping at the right end of the string is defined and the optimal damping value is derived, which is of considerable practical interest in vibration suppression at boundaries for axially traveling materials.

Step Length Estimation with Wearable Wrist Sensor using ANN.

Step Length is an important metric that can be used for the analysis and assessment of the gait. Proper dynamical models are not available in current literature associated with the wrist that can adequately determine the step length using recursive estimation techniques. This study presents a method to estimate the step length using angular velocity data from the wrist sensor. The technique maps the dynamical region corresponding to periods of activity of the gait manifested in angular velocity from the inertial measurement unit located at the wrist to that of the thigh using an artificial neural network, upon which an unscented Kalman filter is used to determine the horizontal position of the foot relative to the hip, and consequently, determine step length. The results for Step Length indicate an average accuracy of 81.8% and 91.1% for the young and elderly, respectively, when compared to a reference system, which, in our study, is data from a treadmill.

Assessing layered HIV prevention programming: optimizing outcomes for adolescent girls and young women.

To assess how exposure to multiple, layered interventions predicts HIV-related outcomes among adolescent girls (15-19 years) and young women (20-24 years) in Kenya. Survey data from adolescent girls and young women (n = 736) with 14-16 months of engagement with DREAMS, a comprehensive HIV prevention program that provides a range of health education, life skills, social protection, and social and behaviour change interventions. Nonparametric recursive partitioning technique - classification and regression tree (CART) - to identify the best predictors (DREAMS interventions) for achieving the desired HIV-related outcomes (consistent condom use and no transactional sex or sexual violence). Among adolescent girls, schooling support reduced the likelihood of engaging in transactional sex, whereas schooling support and exposure to parenting program reduced the likelihood experiencing sexual violence. Likelihood of consistent condom use increased among adolescent girls with exposure to preexposure prophylaxis (PrEP), schooling support, and the violence prevention programming. Among young women, multiple pathways reduced the likelihood of engaging in transactional sex: exposure to the male sexual partner program; exposure to the youth fund program; exposure to the violence prevention program; or exposure/engagement with schooling support, parenting programming, and the youth fund program. For young women, consistent condom use increased with schooling support and male partner engagement. Additionally, engagement in violence prevention program and male partner engagement increased the likelihood of not experiencing sexual violence among young women. Exposure to a combination of DREAMS interventions predicted outcomes that can reduce HIV risk among AGYW, though the pathways differed by outcome and age group.

Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data.

The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model’s hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model.

String indexing for top-k close consecutive occurrences

The classic string indexing problem is to preprocess a string S into a compact data structure that supports efficient subsequent pattern matching queries, that is, given a pattern string P, report all occurrences of P within S. In this paper, we study a basic and natural extension of string indexing called the string indexing for top-k close consecutive occurrences problem (Sitcco). Here, a consecutive occurrence is a pair (i,j), i<j, such that P occurs at positions i and j in S and there is no occurrence of P between i and j, and their distance is defined as j−i. Given a pattern P and a parameter k, the goal is to report the top-k consecutive occurrences of P in S of minimal distance. The challenge is to compactly represent S while supporting queries in time close to the length of P and k. We give three time-space trade-offs for the problem. Let n be the length of S, m the length of P, and ϵ∈(0,1]. Our first result achieves O(nlog⁡n) space and optimal query time of O(m+k). Our second and third results achieve linear space and query times either O(m+k1+ϵ) or O(m+klog1+ϵ⁡n). Along the way, we develop several techniques of independent interest, including a new translation of the problem into a line segment intersection problem and a new recursive clustering technique for trees.

Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).

Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector’s performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.

A tracking control design for linear motor using robust sliding mode learning control

&lt;div id="i4c-draggable-container" style="position: fixed; z-index: 1499; width: 0px; height: 0px;"&gt;&lt;div class="resolved" style="all: initial;" data-reactroot=""&gt; &lt;/div&gt;&lt;/div&gt;&lt;p&gt;In this paper, a tracking robust sliding mode learning control (SMLC) is proposed for a linear motor (LM) system. The proposed controller approach, SMLC, can guarantee a zero tracking error in the absence and presence of system uncertainties of the LM system. Unlike other classical sliding mode control (CSMC), the proposed control system is designed without any prior knowledge of the system perturbation which would facilitate control design and simplify its practical applications. To this end, a recursive learning technique is used which simultaneously adapted based on the previous information of the closed loop system stability. The system stability and convergence analysis are rigorously proved in the sense of the Lybenouve criteria. Finally, simulations results are presented to demonstrate the validity and effectiveness of the SMLC over the CSMC in terms of tracking performance and chattering alleviation.&lt;/p&gt;

An adaptive subspace data-driven method for nonlinear dynamic systems

Quality-related data-driven fault detection methods have been universally recognized by researchers in industry and academia. However, the detection performance of the common data-driven method will degrade if it cannot keep up with the dynamic shifts of the production process. In this paper, an adaptive kernel principal component regression (AKPCR) method is thus proposed to process the nonlinear feature and the time-varying property in the system. The approximate Hilbert space approach is provided to update the projection matrix based on the innovation information from the new data for the nonlinear high-dimensional feature space. Besides, the recursive post-decomposition technique is created from the updated projection matrix to better monitor the quality state. Compared with the fixed threshold, an adaptive one is designed in this study to detect the fault encountered in the time-varying system, which can decrease false alarms in fault detection. Finally, the Tennessee Eastman case (TEC) and the waste water treatment system (WWTS) are leveraged to illustrate the reliability of the proposed approach.

A Fast Matrix Compression Method for Large Scale Numerical Modelling of Rotationally Symmetric 3D Passive Structures in Fusion Devices

This paper illustrates the development of a recursive QR technique for the analysis of transient events, such as disruptions or scenario evolution, in fusion devices with three-dimensional conducting structures using an integral eddy current formulation. An integral formulation involves the solution, at each time step, of a large full linear system. For this reason, a direct solution is impractical in terms of time and memory consumption. Moreover, typical fusion devices show a symmetric/periodic structure. This can be properly exploited when the plasma and other sources possess the same symmetry/periodicity of the structure. Indeed, in this case, the computation can be reduced to only a single sector of the overall structure. In this work the periodicity and the symmetries are merged in the recursive QR technique, exhibiting a huge decrease in the computational cost. Finally, the proposed technique is applied to a realistic large-scale problem related to the International Thermonuclear Experimental Reactor (ITER).

An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes.

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

Sequential detection of a temporary change in multivariate time series

In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of the propagation of an event over a field of sensors, we assumed that the change in the statistical properties in the monitored signals can only be temporary. Unfortunately, to our best knowledge, existing recursive and simple detection techniques such as the ones based on the cumulative sum (CUSUM) do not consider the temporary aspect of the change in a multivariate time series. In this paper, we propose a novel simple and efficient sequential detection algorithm, named Temporary-Event-CUSUM (TE-CUSUM). By combining with a new adaptive way to aggregate local CUSUM variables from each data stream, we empirically show that the TE-CUSUM has a very good detection rate in the case of an event passing through a field of sensors in a very noisy environment.

Correcting optical character recognition result via a novel approach

Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.

Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

In this study, a wavelet recurrent fuzzy neural network is used to conduct in-depth research and analysis on the real-time regulation of physical training intensity. Firstly, an inter-process control technique is proposed to solve the problem of incomplete control flow graph construction caused by the inability to effectively collect all program control flow information in the process of static analysis, in preparation for the research of fuzzy testing technique. Next, a wavelet recursive fuzzy neural network-guided fuzzy testing technique is proposed to solve the problem of fuzzy tests falling into invalid variation due to the lack of directionality in the fuzzy testing process. Each neuron in the feedforward network is divided into different groups according to the order of receiving information. Each group can be regarded as a neural layer. The neurons in each layer receive the output of the neurons in the previous layer and output to the neurons in the next layer. The empirical data show that injury-preventive fitness training can effectively improve all physical qualities in the first phase of preparation and can effectively maintain the physical state and effectively contribute to their abilities during the competition period, and its injury-preventive fitness training interventions were verified by statistical analysis to have a dangerous main effect on their pre and post-test performance. Therefore, it is still not possible to determine its correlation with the coordination and improvement of the athletes' physical fitness, and the integration of the basic physical training and rehabilitation physical training systems, making this theory a new special training theory.

Modelling and forecasting based on recursive incomplete pseudoinverse matrices

Time series modelling has a wide spectrum of applications in several fields including engineering and finance. Most traditional modelling techniques rely on assumptions related to the input data and manual pre-processing, based on user observations, rendering them unsuitable for analysing time-series with varying characteristics automatically, while more general modelling techniques usually require increased computational work for application and tuning. Recently, a general modelling framework based on a recursive Schur complement technique, that utilizes an adaptively determined set of basis functions, has been proposed. Herewith, a novel modified approach based on recursive incomplete pseudoinverse matrices in conjunction with preconditioned iterative methods for large datasets, is proposed. This sparse approach greatly reduces storage requirements and the recursive nature of the procedure avoids recomputation of the preconditioner after addition of a new basis function. Moreover, update of the coefficients for a different window of data and predefined basis functions can be performed utilizing the incomplete pseudoinverse matrix as preconditioner. The case of sinusoidal basis functions is presented along with a novel adaptive frequency estimation technique. The stability of the resulting model is discussed with respect to the choice of basis functions. The case of basis derived from machine learning techniques is also discussed. Numerical results are given depicting the applicability, generality and effectiveness of the proposed technique. Comparative results with other methods show forecasting RMSE improvement between 7% to 80%, for the majority of the chosen time series.

Numerical treatment of multi-term time fractional nonlinear KdV equations with weakly singular solutions

ABSTRACT The main aim of this work is to construct an efficient recursive numerical technique for solving multi-term time fractional nonlinear KdV equation. The fractional derivatives are defined in Caputo sense. A modified Laplace decomposition method is introduced to approximate the solution. The Adomian polynomials play an important role to execute such a recursive process. In addition, the mathematical importance and some applications of KdV equation are discussed. The approximate solution obtained by the proposed method can be expressed in the form of an infinite convergent series. The experimental evidences demonstrate the effectiveness of the proposed method.

Linearly presented modules and bounds on the Castelnuovo-Mumford regularity of ideals

We estimate the Castelnuovo-Mumford regularity of ideals in a polynomial ring over a field by studying the regularity of certain modules generated in degree zero and with linear relations. In dimension one, this process gives a new type of upper bounds. By means of recursive techniques this also produces new upper bounds for ideals in any dimension.

A time-scale transformation approach to prescribed-time stabilisation of non-holonomic systems with inputs quantisation

This article addresses the problem of global stabilisation using state feedback for a kind of uncertain non-holonomic systems in chained form. Two distinct characteristics of this study are that the considered system suffers from the input-quantised actuator, and the system states are expected to converge to zero in prescribed finite time. To address these, a time-scale transformation, that can change the original stabilisation into the finite-time stabilisation of the transformed one, is first introduced. Then, under the new framework of equivalent transformation, a quantised state feedback controller that achieves of the performance requirements is developed with the aid of the recursive technique. Finally, simulation results of a unicycle-type mobile robot are provided to confirm the efficacy of the proposed approach.

Magnetic Viscosity Effect in Magnetic-Source Time-Domain Electromagnetic Surveys

The effect of magnetic viscosity (MV) has continued to be observed with the development and application of the time-domain electromagnetic method (TDEM). Field and laboratory data show that the MV effect is characterized by a -1±0.4 scope power-law delay in the late stage of electromagnetic response. Research on the MV effect can improve the detection accuracy of TDEM and assist in prospecting for ferromagnetic minerals. Most of the studies are based on the Chikazumi susceptibility model and the one-dimensional modeling method. However, the late-stage electromagnetic response shows a -1 power-law delay, which is inconsistent with the measured data. The log-uniform distribution of relaxation time τ in the Chikazumi model is not always appropriate. This study considers the Cole-Cole susceptibility model with a log-normal distribution of relaxation time. The three-dimensional (3D) modeling method of the MV effect is raised based on the rational function approximation algorithm and recursive convolution technique; the control equations and iterative process were adjusted based on finite-different time-domain (FDTD) method. The effectiveness was verified via half-space and layered models; the effects of susceptibility parameters on the response were clarified; moreover, the MV effect of the 3D anomalous model was analyzed. Our method can model the fractional propagation process of the MV effect more efficiently and help to improve the prospecting accuracy of the TDEM method under complex magnetic geological conditions.

Modified cuckoo search algorithm: Feature subset selection &amp;Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

With the recent advances in knowledge, the complication of multimedia has increased expressively and new areas of research have opened up in search of new multimedia content. Content-based image retrieval (CBIR) are used to extract images associated with image queries (IQs) from huge databases. The CBIR schemes accessible at present have limited functionality because they only have a partial number of functions. This document presents an improved cookie detection algorithm with coarse sentences for processing large amounts of data using selected examples. The improved cuckoo detection algorithm mimics the behavior of brood attachment parasites in some cuckoo species, including some birds. Modified cuckoo recognition uses approximate set theory to create a fitness function that takes into account the sum of features and the quality of classification as a small amount. For an image entered as IQ from a database, distance metrics are used to find the appropriate image. This is the central idea of CBIR. The projected CBIR method is labelled and can extract shape features based on the RGB color using the and canny Edge (CED) and neutrosophic clustering algorithm scheme. After YCbCrcolor cut, and the CED to get the features to extract the vascular matrix. The combination of these techniques improves the efficiency of the CBR image recovery infrastructure. In this thesis recursive neural network techniques are used to measure the similarity. In addition, the accuracy of the results is: The recall score is measured to evaluate system performance. The proposed CBIR system provides more precise and accurate values than the complex CBIR system.

Recursive elimination current algorithms and a distributed computing scheme to accelerate wrapper feature selection

Feature selection (FS) for classification tasks in machine learning and data mining has attracted significant attention. Recently, increasing metaheuristic optimization algorithms have been applied to solve wrapper FS problems. Nevertheless, the algorithms that improve existing optimizers inevitably bring higher complexity and require higher computational cost in wrapper mode. In this work, we present recursive elimination current (REC) algorithms, a novel set of algorithms for wrapper FS, which consists of the simplest feature subset representation, an ingenious structure enlightened by the recursion technique in computer science and necessary components. To some extent, the proposed algorithms, recurrent REC (REC2) and distributed REC (DiREC), dispose of the issues, including but not limited to keeping diversity of population and scalability for high-dimensionality, which are often discussed in metaheuristics-based FS research. Thereinto, DiREC is a distributed computing scheme proposed to accelerate the FS process by distributing the tasks to different computing units in a cluster of computers. A series of experiments were carried out on several representative benchmark datasets, such as Madelon from UCI machine learning repository, using REC2 and DiREC with various numbers of logical processors. It is demonstrated that the proposed algorithms perform efficiently in wrapper mode, and the distributed computing scheme is effective and yields computational time savings.