Increase In Number Of Iterations Research Articles

Two-stage benefits of internal and external noise to enhance early fault detection of machinery by exciting fractional SR

For early mechanical fault diagnosis, stochastic resonance (SR) breaks the previous perception that “noise is useless” and uses internal noise or external noise to enhance weak fault characteristics. Moreover, the fractional-order derivative can reinforce noise-enhanced weak fault detection. However, in the face of extremely low signal-to-noise ratio conditions, the enhancement effect of fractional SR induced by a single excitation of internal noise or external noise is unsatisfactory. To solve the above drawbacks, this paper investigates two-stage benefits of both internal and external noise to enhance early fault detection of machinery by exciting parallel array of fractional SR, and using the signal-to-noise ratio (SNR) to adjust these parameters of fractional SR. Then, two experiments including rolling element bearings and gearboxes were performed to validate it. Experimental results show that the proposed method takes on obvious detection ability for low SNR signals, where the amplitude at the fault characteristic frequency is amplified by beyond 300 times in bearing and gearbox fault experiments than one-stage those. In addition, it is also found that as the noise intensity and the number of iterations increase, the amplitude at the fault characteristic frequency tends to the peak value and then falls into a saturation value. Finally, compared with empirical mode decomposition (EMD), the proposed method can amplify the amplitude at the fault characteristic frequency beyond 1000 times in bearing and gearbox fault experiments. It was concluded that the proposed method has obvious advantages in extracting weak fault characteristics of machinery submerged by strong background noise.

Exploration on the practice process of modernization of vocational education governance system based on big data

Abstract In the era of big data, the integration of teaching and information technology has become a new trend in the development of modernized vocational education. Taking the big data majors of vocational college M as an example, this study explores the modernization of vocational education governance system, establishes a vocational education resource recommendation model based on K-Means clustering and FunkSVD algorithm, applies the model to analyze the students’ vocational abilities in terms of user profiles, and verifies the performance stability of the resource recommendation model through continuous iterative training of the model. Comparing the changes in students’ vocational abilities before and after using the resource recommendation model, the effects of the vocational education resource recommendation model are explored. It is finally concluded that the vocational skills of the selected students can be categorized into three types: the first type of students has the most muscular comprehensive strength and the highest proportion, 62.15%. The RMSE value of the resource recommendation model gradually decreases with the increase of data volume and iteration number. The RMSE value decreases by 0.06 when the data volume increases 10 times. The vocational ability of the selected students increases by about 0.05~0.15 after the resource recommendation, which verifies the recommendation effect of the model. The study can be used to improve students’ vocational competence and provide intelligent new ideas for the governance system of vocational education.

CFD simulation and experimental analysis of solid-liquid mixing characteristics in a stirred tank with a self-similarity impeller

BackgroundThe nonlinear geometry could create a longer turbulence production region and higher turbulence intensities compared with conventional geometry. A self-similarity impeller based on Hibert curves was introduced to intensify the solid-liquid chaotic mixing process in this work. MethodsThe hydrodynamics of solid-liquid mixing process in a stirred tank with pitched-blade impeller, self-similarity 1 impeller and self-similarity 2 impeller were studied through experimental analyses and numerical simulation. Significant findingsResults showed that self-similarity impeller could consume less power consumption compared with pitched-blade impeller at the same impeller speed. Self-similarity impeller could improve the largest Lyapunov exponent (LLE) value compared with pitched-blade impeller under the constant power consumption, and the LLE value continued to increase with the increase of the number of self-similar iterations. Meanwhile, self-similarity impeller could improve the solid particle suspension quality compared with pitched-blade impeller under the constant power consumption, and increasingly so with the self-similar iteration number. Moreover, self-similarity impeller could destroy the large tailing vortex and recirculation zone, and enhance the turbulent kinetic energy dissipation rate by the jet flows through the concave-convex edges compared with pitched-blade impeller, and this phenomenon became obvious with the increase of self-similar iteration number.

Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links.

Performance analysis and comparison of IDEAL and SIMPLERR algorithms for incompressible fluid flow and heat transfer problems

In this paper, the numerical performances of the algorithm IDEAL proposed by the present authors in 2008, and the recently proposed (2017) SIMPLERR are systematically analyzed and compared via six incompressible fluid flow and heat transfer problems. It is found that the two algorithms are almost identical when the second inner iteration number of IDEAL is fixed at 2. In general, the robustness and convergence of IDEAL are much better than SIMPLERR. The reason why the performance of SIMPLERR deteriorates with the increase of the first inner iteration number is analyzed and detailed comparisons in the robustness and computing time are presented.

Study on hydrodynamics characteristics in a gas-liquid stirred tank with a self-similarity impeller based on CFD-PBM coupled model

BackgroundIn general, radial impeller has been the most widely employed for the gas-liquid mixing process in the stirred tank. However, gas cavities are easy to form behind the impeller blades, which results in the waste of power consumption, the limited gas handling capacity of impeller and the poor dispersion of gas. Based on self-similarity property of nonlinear theory, a type of self-similarity impeller was employed to strengthen gas-liquid dispersion process in this work. MethodsThe hydrodynamics of gas-liquid dispersion process in a stirred tank with Rushton turbine and self-similarity impeller were comparatively investigated by computational fluid dynamics (CFD) simulation combined with a population balance model (PBM). The gas holdup distribution, gas cavities, relative power demand (RPD), bubble size distribution and turbulent intensity analysis in the gas-liquid dispersion process were predicted. Significant findingsResults showed that self-similarity impeller could destroy the gas cavity structure, reduce the negative pressure zone and gas accumulation, enhance the relative power demand (RPD) and improve the uniformity of gas holdup distribution compared with Rushton turbine, and the gas-liquid mixing efficiency can be further improved with the increase of the self-similar iteration number of self-similarity impeller. The proper increase of impeller speed was beneficial to overcome gas flooding and improve the gas-liquid two phase dispersion uniformity. The increase of gas flow rate obviously increased the gas holdup in liquid phase. Meanwhile, self-similarity impeller could increase the uniformity of bubble size compared with Rushton turbine, and increasingly so with the increase of self-similar iteration number of self-similarity impeller. The uniformity of bubble size was improved with the increase of impeller speed and was decreased with the increase of gas flow rate. In addition, self-similarity impeller could significantly enhance the turbulent intensity and turbulent kinetic energy dissipation rate by the jet flows through the concave-convex edges, which was beneficial to the gas dispersion process.

Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition.

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition since it is resistant to camouflage and contains abundant physiological information. However, EEG signals are non-stationary and have low signal-noise-ratio, making it more difficult to decode in comparison with data modalities such as facial expression and text. In this paper, we propose a model termed semi-supervised regression with adaptive graph learning (SRAGL) for cross-session EEG emotion recognition, which has two merits. On one hand, the emotional label information of unlabeled samples is jointly estimated with the other model variables by a semi-supervised regression in SRAGL. On the other hand, SRAGL adaptively learns a graph to depict the connections among EEG data samples which further facilitates the emotional label estimation process. From the experimental results on the SEED-IV data set, we have the following insights. 1) SRAGL achieves superior performance compared to some state-of-the-art algorithms. To be specific, the average accuracies are 78.18%, 80.55%, and 81.90% in the three cross-session emotion recognition tasks. 2) As the iteration number increases, SRAGL converges quickly and optimizes the emotion metric of EEG samples gradually, leading to a reliable similarity matrix finally. 3) Based on the learned regression projection matrix, we obtain the contribution of each EEG feature, which enables us to automatically identify critical frequency bands and brain regions in emotion recognition.

Iterative learning based consensus control for distributed parameter type multi-agent differential inclusion systems with time-delay

This paper investigates consensus control problem for a class of distributed parameter type multi-agent differential inclusion systems with state time-delay by utilizing iterative learning control (ILC). Unlike most ILC literature of nonlinear distributed parameter systems that require an identical virtual leader, the virtual leader is iteratively varying in this work and can be described by any follower agent. Both P -type closed-loop and D -type open-loop ILC are proposed for the distributed parameter type multi-agent differential inclusion systems, which enable the output trajectories of each agent to achieve consensus in the sense of W 1 , 2 norm as the iteration number increases. Meanwhile, consensus control theorems for these protocols are established by using Steiner-type selector, Green's formulas and some technical inequalities. Finally, numerical simulations testify the validity of the proposed methods.

Design of impeller blades for intensification on fluid mixing process in a stirred tank

BackgroundMany studies reported that the nonlinear geometry could create a longer turbulence production region and higher turbulence intensities compared with conventional geometry. The self-similarity impeller based on Hibert curves was introduced to intensify the fluid mixing process in this work. MethodsThe mixing performance of Rushton turbine (RT) impeller and self-similarity impeller were evaluated using experimental analyzes and computational fluid dynamics (CFD) with detached eddy simulation (DES) approach. The chaotic mixing characteristics, mixing time, power consumption, trailing vortices structures, turbulence intensity, turbulent kinetic energy dissipation rate and velocity magnitude distribution were investigated. Significant findingsResults showed that self-similarity impeller can effectively increase the largest Lyapunov exponent (LLE) and multiscale entropy (MSE) of mixing system, shorten the fluid mixing time and decrease the mixing number (η) compared with RT impeller under the constant power consumption, and an improvement was achieved in mixing efficiency with the increase of self-similar iteration number of self-similarity impeller. Meanwhile, the reductions in the power number (Np) of 34.6% with self-similarity 1 impeller and 51.9% with self-similarity 2 impeller were achieved in a wide Re range compared with RT impeller. Two large recirculation zones and trailing vortices were easy to form at the suction side of rectangular impeller blade, which can be destroyed and decomposed into smaller ones through the jet flows produced by the concave-convex structure of self-similarity impeller. In addition, self-similarity impeller can improve the uniformity of fluid turbulence intensity, enhance the turbulent kinetic energy dissipation rate and increase the axial circulation capacity in the stirred tank, and increasingly so with the self-similar iteration number.

An Improved Machine Translation Model and its Application in Japanese Multi-Context Translation

In order to further improve the application of machine translation model in Japanese translation, analytic analysis method is adopted to optimize the original machine translation model. The improved machine translation model is used to analyze and describe Japanese translation. Finally, the optimized machine translation model is used to analyze Japanese multicontext. The relevant indexes and parameters were extracted and verified, and finally the model was verified by relevant experiments. The results show that the vector variation graph with different parameters can be divided into slow decline stage, stable change stage, and fast decline stage according to the increase of iteration number and the influence of corresponding change trend. In addition, it can be seen from the value of PE curve that the influence of parameter pe is the least, while the influence of corresponding re parameter is the greatest. The multicontext index of Japanese has the greatest influence on Japanese fluency and the least influence on Japanese keywords, and the trend of influence is parabolic. The application curve of the optimized machine translation model to Japanese in multiple contexts shows that different parameters have different effects on Japanese, which should be represented by the positive parameter V. Finally, the accuracy of the model is verified by experimental data. The above research can provide support for the application of machine learning in different fields and also provide research ideas for the multicontext translation of Japanese.

Extension of the R–L algorithm for [formula omitted]-ray spectra deconvolution and its experimental results

The Richardson–Lucy (R–L) algorithm is widely used for γ-ray spectra deconvolution. In this study, we considered the R–L algorithm as an iteration process of an optimization problem and extended it with a universal function. Three iterative formulae are given based on common elementary functions: the exponential R–L algorithm, the logarithmic R–L algorithm, and the power R–L algorithm. Simulated and experimental 152Eu spectra were used to test the effects of the proposed algorithms. Results show that all the three algorithms make the objective function value decrease monotonically with the increase of iteration number. With proper parameters, the extended algorithms achieve faster convergence than the original R–L algorithm and the MAP algorithm. The power R–L algorithm with m less than but close to 2 shows the fastest convergence rate. This indicates the proposed extension is an effective improvement. Also, the method could be used for other objective functions, which provides a new idea for constructing better iterative algorithms for spectrum or image deconvolution, such as chromatography, infrared spectroscopy, Raman spectroscopy, X-ray fluorescence spectroscopy and medical imaging.

A Modified Search and Rescue Optimization Based Node Localization Technique in WSN

Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN. The major aim of the MSRO-NLT technique is to determine the positioning of the unknown nodes in the WSN. Since the traditional search and rescue optimization (SRO) algorithm suffers from the local optima problem with an increase in number of iterations, MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique. The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm. In order to validate the effective node localization performance of the MSRO-NLT algorithm, a set of simulations were performed to highlight the supremacy of the presented model. A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Initial-Rectification Barrier Iterative Learning Control for Pneumatic Artificial Muscle Systems With Nonzero Initial Errors and Iteration-Varying Reference Trajectories

Pneumatic artificial muscle actuators possess great potential in compliant rehabilitation devices since they are flexible and lightweight. The inherent high nonlinearities, uncertainties, hysteresis and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. The angle tracking problem based on iterative learning control technology is considered in this work. This research proposes a new initial-rectification adaptive iterative learning control scheme for a pneumatic artificial muscle-actuated device with nonzero initial errors and iteration-varying reference trajectories. A barrier Lyapunov function is used to deal with the constraint requirement. A new initial rectification construction method is given to solve the nonzero initial error problem. Nonparametric uncertainties in the system are approximated by using a neural network, whose optimal weight is estimated by using difference learning method. As the iteration number increases, the system states of angle and angular velocity can accurately track the reference trajectories over the whole interval, respectively. In the end, the simulation results show excellent trajectory tracking performance of the iterative learning controller even if the reference trajectories are non-repetitive over the iteration domain.

Barrier Adaptive Iterative Learning Control for Tank Gun Control Systems Under Nonzero Initial Error Condition

In this paper, a barrier adaptive iterative learning control scheme is proposed to solve the trajectory-tracking problem for tank gun control systems under nonzero initial error condition. A novel construction method of rectified reference trajectory is presented for dealing with the initial position problem of iterative learning control for tank gun control systems. With a quadratic form barrier Lyapunov function adopted to controller design, the quadratic form of system error is constrained within the preset range during each iteration. Adaptive iterative learning control technique and robust control technique are jointly used to compensate for the parametric/nonparametric uncertainties and nonsymmetric deadzone nonlinearity. As the iteration number increases, the system state of tank gun control systems may accurately track the rectified reference trajectory, which leads to a excellent tracking performance during the part operation interval of tank gun control systems. Simulation results are presented to verify the effectiveness of the proposed barrier adaptive iterative control scheme.

High-Order Internal Model Based Barrier Iterative Learning Control for Time-Iteration-Varying Parametric Uncertain Systems With Arbitrary Initial Errors

In this paper, a high-order internal model based adaptive iterative learning control scheme is proposed to solve the trajectory tracking problem for a class of nonlinear systems with time-iteration-varying parametric uncertainties which are generated from a high-order internal model. A time-varying boundary layer is constructed to remove the nonzero initial error condition in ILC design. An adaptive iterative learning law is designed to deal with the time-iteration-varying parametric uncertainties. For improving the robustness and safety, a barrier Lyapunov function is adopted to controller design, thus making the filtering error constrained during each iteration. Even there exist nonzero initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as the iteration number increases. Simulation results show the effectiveness of the propose high-order internal model based filtering-error constraint adaptive learning scheme.

Fractional Differential Equation-Based Instantaneous Frequency Estimation for Signal Reconstruction

In this paper, we propose a fractional differential equation (FDE)-based approach for the estimation of instantaneous frequencies for windowed signals as a part of signal reconstruction. This approach is based on modeling bandpass filter results around the peaks of a windowed signal as fractional differential equations and linking differ-integrator parameters, thereby determining the long-range dependence on estimated instantaneous frequencies. We investigated the performance of the proposed approach with two evaluation measures and compared it to a benchmark noniterative signal reconstruction method (SPSI). The comparison was provided with different overlap parameters to investigate the performance of the proposed model concerning resolution. An additional comparison was provided by applying the proposed method and benchmark method outputs to iterative signal reconstruction algorithms. The proposed FDE method received better evaluation results in high resolution for the noniterative case and comparable results with SPSI with an increasing iteration number of iterative methods, regardless of the overlap parameter.

Iterative weighted active transfer learning hyperspectral image classification

The hyperspectral image (HSI) has the characteristics of high resolution, a large amount of data, and a high correlation of bands. In the many HSI processing technologies, image classification is the most basic one. Supervised classification is the most effective and common classification method. However, to achieve the ideal classification effect, supervised classification needs a large number of labeled samples, which requires a lot of time and labor. To solve this problem, we combine active learning (AL) and transfer learning and propose an iterative weighted framework based on active transfer learning. First, we solve the optimal reconstruction matrix and projection matrix by minimizing the reconstruction error. Then, we project labeled samples from the source and target domains into the common subspace. In the iteration of the common subspace, the classifier performance will be better with the increase of iteration number. In each iteration, the weighted strategy is adopted to weigh the samples of the source domain. In this way, valuable source domain labeled samples will get a larger weight, so as to help the classification of target domain samples better. At the same time, AL is used to screen out a certain number of samples of the target domain for manual labeling, which is added to the labeled samples set. Experiments on three data sets demonstrate the effectiveness and reliability of the proposed method.

Convex combination-based consensus analysis for intuitionistic fuzzy three-way group decision

A three-way decision (3WD) with group consensus using intuitionistic fuzzy sets (IFSs) involves two pivotal decision steps: achieving a consensus of loss functions and determining the threshold pair in 3WD. We focus on these decision steps and propose a convex combination-based approach to a three-way intuitionistic fuzzy group decision (3WIFGD) considering a group consensus. First, a similarity measure between IFSs is introduced to define a group consensus index (GCI) for an expert group based on loss functions. Then, an automated algorithm is designed with the GCI-based convex combination strategy to improve the group consensus of loss functions. Moreover, we theoretically prove that the GCI in this algorithm is improved and even converges linearly to “1” as the iteration number increases. Second, based on the aggregated collective consensus loss functions, we construct the optimization model pair by extending the existing models and prove its unique solution, leading to the thresholds. Third, a two-decision-steps-based method for 3WIFGD is developed to capture the rules underlying a group consensus. Finally, an illustrative example and its related comparisons are demonstrated to show the validity of our method.

Iterative learning control for multi‐agent systems with noninstantaneous impulsive consensus tracking

AbstractIn this paper, we consider the consistent tracking problem of noninstantaneous impulsive multi‐agent systems. Take advantage of the repeatability of tracking tasks and the learning ability of each agent, we show that all agents of linear systems are driven to achieve a given asymptotical consensus as the number of iteration increases by using the standard ‐type learning law with the initial state learning rule. In addition, for nonlinear systems, we use ‐type learning law with the initial state learning rule to prove that all agents are driven to achieve a given asymptotical consensus as the number of iteration increases. Finally, two numerical examples are given to verify the effectiveness of our algorithm.

A hybrid weighted fuzzy approach for brain tumor segmentation using MR images

Human brain tumor detection and classification are time-consuming however vital tasks for any medical expert. Assistance via computer aided diagnosis is commonly used to enhance diagnosis capabilities attaining maximum detection accuracy. Despite significant research, brain tumor segmentation is still an open challenge due to variability in image modality, contrast, tumor type, and other factors. Many great works ranging from manual, semiautomatic, or fully automatic tumor segmentation with magnetic resonance (MR) brain images are available, however, still creating a space for developing efficient and accurate approaches in this domain. This manuscript proposes a hybrid weighted fuzzy k-means (WFKM) brain tumor segmentation algorithm using MR images to retrieve more meaningful clusters. It is based on fuzzification of weights which works on spatial context with illumination penalize membership approach which helps in settling issues with pixel’s multiple memberships as well as exponential increase in number of iterations. The segmented image is further utilized for successful tumor type identification as benign or malignant by means of SVM. Experimentation performed on MR images using Digital Imaging and Communications in Medicine (DICOM) dataset shows that fusion of proposed WFKM and SVM outperforms many existing approaches. Further, performance evaluation parameters show that the proposal produces better overall accuracy. Results on variety of images further prove applicability of the proposal in detecting ranges and shapes of brain tumor. The proposed approach excels qualitatively as well as quantitatively reporting an average accuracy of 97% on DICOM dataset with total number of images varying from 100 to 1000.