Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.

Fully Distributed Learning for Deep Random Vector Functional-Link Networks

In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.

基于Vector Random Decrement技术和特征系统实现算法ERA的模态参数识别

现代的大型复杂结构,如大坝、高层建筑、桥梁及海洋平台等,处于复杂的环境载荷作用下,这些环境载荷往往是无法测量的。在仅有输出响应时,应用随机减量法RDT获得自由衰减响应信号,而后用时域复指数拟合法、ITD法、特征系统实现算法ERA等算法获得结构的模态参数是一种有效的方法。但在数据量有限时,随机减量函数的平均次数过少,导致RD函数的收敛性较差。为此提出了利用Vector Random Decrement技术(VRDT)提取自由衰减响应信号,而后利用特征系统实现算法ERA求得模态参数的方法,新算法能够有效地提高模态参数识别精度。数值算例验证了所提算法的有效性。

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.

Dispersion of the Mechanical Parts Performance Indicators Based on the Concept of Random Vector

To solve the precision and reliability problem of various machinery equipments and military vehicles, some military organisations, the industrial sector and the academia at home and abroad begin to pay attention to the statistical distribution of machining dimensions, material properties and service loads, and the system reliability optimization design with constraints and reliability optimization design of various mechanical parts is studied in this way. However, the above researches focus on solving the strength and the life problem, and no studies have been done on the discrete degree and discrete pattern of other performance indicators. The concept of using a random vector to describe the mechanical parts performance indicators is presented; characteristics between the value of the vector variance matrix determinant and the sum of the diagonal covariance matrix in describing the performance indicators of vector dispersion are studied and compared. A clutch diaphragm spring is set as an example, the geometric dimension indicator is described with random vector, and the applicability of using variance matrix determinant and variance matrix trace of geometric dimension vector to describe discrete degree of random vector is studied by using Monte-Carlo simulation method and component discrete degree perturbation method. Also, the effects of different components of diaphragm spring geometric dimension vector on the value of covariance matrix determinant and the sum of covariance matrix diagonal of diaphragm spring performance indicators vector are analyzed. The present study shows that the impacts of the dispersion of diaphragm spring cone angle on every performance dispersion are all ranked first, and far exceed that of other dimension dispersion. So it must be strictly controlled in the production process. The result of the research work provides a reference for the design of diaphragm spring, and also it presents a proper method for researching the performance of other mechanical parts.

The Estimation of Radial Exponential Random Vectors in Additive White Gaussian Noise

Image signals are always disturbed by noise during their transmission, such as in mobile or network communication. The received image quality is significantly influenced by noise. Thus, image signal denoising is an indispensable step during image processing. As we all know, most commonly used methods of image denoising is Bayesian wavelet transform estimators. The Performance of various estimators, such as maximum a posteriori (MAP), or minimum mean square error (MMSE) is strongly dependent on correctness of the proposed model for original data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is important in wavelet-based image denoising. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each subband with multivariate Radial Exponential probability density function (PDF) with local variances. Generally these multivariate extensions do not result in a closed form expression, and the solution requires numerical solutions. However, we drive a closed form MMSE shrinkage functions for a Radial Exponential random vectors in additive white Gaussian noise (AWGN). The estimator is motivated and tested on the problem of wavelet-based image denoising. In the last, proposed, the same idea is applied to the dual-tree complex wavelet transform (DT-CWT), This Transform is an over-complete wavelet transform.

The Comparison between Random Forest and Support Vector Machine Algorithm for Predicting β-Hairpin Motifs in Proteins

Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.

Efficient Global Threshold Vector Outlyingness Ratio Filter for the Removal of Random Valued Impulse Noise

This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with the algorithm that breaks the images into various decomposition levels using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using the outlyingness of the pixel. This algorithm has the capability of differentiating high frequency pixels and the “noisy pixel” using the threshold as well as window adjustments. The damage and the loss of information are prevented by means of interior mining. This global threshold based algorithm uses different thresholds for different quadrants of DWT and thus helps in recovery of noisy image even if it is 90% affected. Experimental results exhibit that this method outperforms other existing methods for accurate noise detection and removal, at the same time chain of connectivity is not lost.

Computational Intensity Prediction Model of Vector Data Overlay with Random Forest Method

Spatial analysis is the core of geographic information system(GIS),of which,spatial overlay of vector data is a major job.Computational intensity of the spatial overlay has a direct effect on the overall performance of the GIS.High precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task.Thus,the paper proposes a novel approach,which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis.Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.

应用背包和无人机LiDAR数据对森林地上生物量估测

激光雷达(LiDAR)技术在林业调查中应用广泛,能够精确获取森林垂直结构信息。利用背包LiDAR结合实地调查样地,验证其替代实地调查的可行性;应用UAV-LiDAR数据,采用多元逐步回归(MSR)、支持向量机(SVM)和随机森林(RF)算法,建立地上生物量估测模型并进行对比分析。研究结果显示:(1)在人工干预下,应用背包LiDAR数据提取的单木参数与实测值高度相关,平均胸径的决定系数(R^(2))为0.98,均方根误差(R_(MSE))为0.35 cm;平均树高的R^(2)为0.96,R_(MSE)为0.63 m。(2)应用背包LiDAR构建的生物量样本,利用UAV-LiDAR建立的AGB估测模型中,随机森林模型表现最佳(R^(2)=0.75,R_(MSE)=23.58 t/hm^(2)),其次是支持向量机模型(R^(2)=0.63,R_(MSE)=30.49 t/hm^(2)),多元逐步回归模型表现最差(R^(2)=0.54,R_(MSE)=35.60 t/hm^(2))。因此,背包LiDAR获取的单木胸径及树高精度较高,可替代实测样地生物量,以扩大样本覆盖范围;应用背包LiDAR数据结合机载LiDAR,可实现较大尺度的森林生物量快速估测,为大范围森林生物量反演提供了一种可行方法。

基于改进的Random Subspace 的客户投诉分类方法

电信业的客户投诉不断增多而又亟待高效处理。针对电信客户投诉数据的特点,提出了一种面向高维数据的改进的集成学习分类方法。该方法综合考虑客户投诉中的文本信息及客户通讯状态信息,基于Random Subspace方法,以支持向量机(Support Vector Machine,SVM)为基分类器,采用证据推理(Evidential Reasoning,ER)规则为一种新的集成策略,构造分类模型对电信客户投诉进行分类。所提模型和方法在某电信公司客户投诉数据上进行了验证,实验结果显示该方法能够显著提高客户投诉分类的准确率和投诉处理效率。

On Utility Maximization with Random Interval Payoffs

This article discusses the problem of utility maximization in a market with random-interval payoffs without short-selling prohibition. A novel expected utility model is given to measure an investor's subjective view toward random interval wealth. Some techniques are proposed to transfer a complex programming involving interval numbers into a simple non-linear programming. Under the existence of the optimal strategy, relations between the optimal strategy and assets' prices are discussed. Some properties of the maximal utility function with respect to the endowment are given.

A RANDOM TRANSPORT-DIFFUSION EQUATION

In this paper we study the integral curve in a random vector field perturbed by white noise. It is related to a stochastic transport-diffusion equation. Under some conditions on the covariance function of the vector field, the solution of this stochastic partial differential equation is proved to have moments. The exact p-th moment is represented through integrals with respect to Brownian motions. The basic tool is Girsanov formula.

Multi-scale prediction of MEMS gyroscope random drift based on EMD-SVR

To improve the prediction accuracy of micro-electromechanical systems(MEMS)gyroscope random drift series,a multi-scale prediction model based on empirical mode decomposition(EMD)and support vector regression(SVR)is proposed.Firstly,EMD is employed to decompose the raw drift series into a finite number of intrinsic mode functions(IMFs)with the frequency descending successively.Secondly,according to the time-frequency characteristic of each IMF,the corresponding SVR prediction model is established based on phase space reconstruction.Finally,the prediction results are obtained by adding up the prediction results of all IMFs with equal weight.The experimental results demonstrate the validity of the proposed model in random drift prediction of MEMS gyroscope.Compared with a single SVR model,the proposed model has higher prediction precision,which can provide the basis for drift error compensation of MEMS gyroscope.

Design and realization of threshold secret sharing scheme with random weights

A new threshold secret sharing scheme is constructed by introducing the concept of share vector, in which the number of shareholders can be adjusted by randomly changing the weights of them. This kind of scheme overcomes the limitation of the static weighted secret sharing schemes that cannot change the weights in the process of carrying out and the deficiency of low efficiency of the ordinary dynamic weighted sharing schemes for its resending process. Thus, this scheme is more suitable to the case that the number of shareholders needs to be changed randomly during the scheme is carrying out.

Randomized Algorithm for Determining Stabilizing Parameter Regions for General Delay Control Systems

This paper proposes a method for determining the stabilizing parameter regions for general delay control systems based on randomized sampling. A delay control system is converted into a unified state-space form. The numerical stability condition is developed and checked for sample points in the parameter space. These points are separated into stable and unstable regions by the decision function obtained from some learning method. The proposed method is very general and applied to a much wider range of systems than the existing methods in the literature. The proposed method is illustrated with examples.

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions.The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible,non-curable,and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention.It is a frequently occurring mental illness that occurs in about 60%–80%of cases of dementia.It is usually observed between people in the age group of 60 years and above.Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal(CN),Mild Cognitive Impairment(MCI)and Alzheimer’s Disease(AD).Alzheimer’s disease is the last phase of the disease where the brain is severely damaged,and the patients are not able to live on their own.Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms.Here,105 number of radiomic features are extracted and used to predict the alzhimer’s.This paper uses Support Vector Machine,K-Nearest Neighbour,Gaussian Naïve Bayes,eXtreme Gradient Boosting(XGBoost)and Random Forest to predict Alzheimer’s disease.The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%.This proposed approach also achieved 88%accuracy,88%recall,88%precision and 87%F1-score for AD vs.CN,it achieved 72%accuracy,73%recall,72%precisionand 71%F1-score for AD vs.MCI and it achieved 69%accuracy,69%recall,68%precision and 69%F1-score for MCI vs.CN.The comparative analysis shows that the proposed approach performs better than others approaches.

An Improved Hybrid Space Vector PWM Technique for IM Drives

In this paper, an improved hybrid space vector pulse width modulation (HSVPWM) technique is proposed for IM (induction motor) drives. The basic principle involved in the proposed random pulse width modulation (RPWM) cuddled SVPWM is amalgamating the pre-calculated switching timings for various sections of hexagonal space vector boundary and the random selection of carrier between two triangular signals, in order to disband acoustic switching noise spectrum with improved fundamental component. The arbitrary selection between triangular carriers, which is decided by digital signal states (Low or High) of the linear feedback shift register (LFSR) based pseudo random binary sequence (PRBS) generator. The SVPWM offers a control degree of freedom in terms of positioning of vectors inside every sampling interval and hence it has six possible variants of the voltage vectors arrangements in each sector. The developed HSVPWM is thoroughly analyzed in using the MATLAB? based simulation for all SVPWM variants. From the simulation and experimental results viz. harmonic spectrum, harmonic spread factor (HSF), total harmonic distortion (THD) etc., and the superiority of the proposed scheme such as better utilization of DC bus and the randomization of the harmonic power are evidenced. For the practical implementation, Xilinx XC3S500E FPGA device has been used.

基于机器学习的“一带一路”投资国别风险预测研究

“一带一路”倡议提出十年间,中国对沿线国家的投资规模持续扩大。然而,企业在抓住机遇,进行“一带一路”沿线国家投资的同时,也需要重点关注“一带一路”投资国别风险。本文从政治、经济、社会和对华关系4个维度构建“一带一路”投资国别风险预测指标体系;运用灰色关联分析计算样本国家的综合风险评价值;基于2012~2022年间“一带一路”沿线国家的数据,利用机器学习构建GA-BP神经网络、支持向量回归和随机森林3种预测模型;通过对比预测精度,确定最佳预测模型,利用2021年的指标数据,对2022年的投资国别风险进行预测。研究结果表明:(1)在“一带一路”投资国别风险的研究背景下,支持向量回归模型预测效果最优,证明机器学习模型能够有效应用于风险管理领域;(2)“一带一路”投资国别风险存在明显的地区差异,中东欧地区和东南亚地区投资国别风险普遍较低,而南亚地区投资国别风险普遍较高,但都存在特例。本文研究结果可为“走出去”企业在“一带一路”沿线国家的投资决策提供参考。