Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

Training with Input Selection and Testing (TWIST) Algorithm: A Significant Advance in Pattern Recognition Performance of Machine Learning

This article shows the efficacy of TWIST, a methodology for the design of training and testing data subsets extracted from given dataset associated with a problem to be solved via ANNs. The methodology we present is embedded in algorithms and actualized in computer software. Our methodology as implemented in software is compared to the current standard methods of random cross validation: 10-Fold CV, random split into two subsets and the more advanced T&T. For each strategy, 13 learning machines, representing different families of the main algorithms, have been trained and tested. All algorithms were implemented using the well-known WEKA software package. On one hand a falsification test with randomly distributed dependent variable has been used to show how T&T and TWIST behaves as the other two strategies: when there is no information available on the datasets they are equivalent. On the other hand, using the real Statlog (Heart) dataset, a strong difference in accuracy is experimentally proved. Our results show that TWIST is superior to current methods. Pairs of subsets with similar probability density functions are generated, without coding noise, according to an optimal strategy that extracts the most useful information for pattern classification.

2D spiral pattern recognition based on neural network covering algorithm

The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

Drainage pattern recognition method considering local basin shape based on graph neural network

Drainage pattern recognition is crucial for geospatial understanding and hydrologic modelling.Currently,drainage pattern recognition methods employ geometric measures of overall and local features of river networks but lack measures of river basin unit shape features,so that potential correlations between river segments are usually ignored,resulting in poor drainage pattern recognition results.In order to overcome this problem,this paper proposes a supervised graph neural network method that considers the local basin unit shape of river networks.First,based on the overall hierarchy of the river networks,the confluence angle of river segments and the shape of river basin units,multiple drainage pattern classification features are extracted.Then,typical drainage pattern samples from the multi-scale NSDI and USGS databases are used to complete the training,validation and testing steps.Experimental results show that the drainage pattern indexes proposed can describe the characteristics of different drainage patterns.The method can effectively sample the adjacent river segments,flexibly transfer the associated pattern features among river segment neighbours,and aggregate the deeper characteristics of the river networks,thus improving the drainage pattern recognition accuracy relative to other methods and reliably distinguishing different drainage patterns.

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.

Fuzzy Neural Model for Flatness Pattern Recognition

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Adaptive learning with guaranteed stability for discrete-time recurrent neural networks

To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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An improved micro-expression recognition algorithm of 3D convolutional neural network

The micro-expression lasts for a very short time and the intensity is very subtle.Aiming at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,simultaneously.The network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training speed.Aiming at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data amplification.Finally,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.

Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.

AN EFFECTIVE LVQ-BASED ALGORITHMFOR ROBUST SPEECH RECOGNITION

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Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established;then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.

基于卷积神经网络与可视图像的类滑动放电模式识别

为了提高机器学习算法对类滑动放电模式识别的准确率,提出了一种基于卷积神经网络(convolutional neuralnetworks,CNN)与可视图像识别电晕放电、弥散放电和类滑动放电等模式的方法。通过选取气体体积流量0~16 L/min、电极间隙2~10 mm、脉冲频率0.5~3 kHz等不同条件下的类滑动放电图像构建图像库,搭建CNN模型并优化影响CNN识别性能的超参数,包括网络层数、全连接层(full connected layer,FC)神经元数、卷积核尺寸以及激活函数类型,最后比较了CNN与决策树(decision tree,DT)算法和随机森林(random decision forests,RF)算法的识别效果。结果表明,CNN识别准确率为100%,高于传统机器学习方法。此外,本文还给出了放电模式及条件参数,通过基于反向传播神经网络(back propagation neural networks,BPNN)的聚类分析算法识别弥散放电和类滑动放电,并且准确率为100%。

航空平台地磁矢量匹配导航算法研究进展

航空地磁矢量导航技术因其具有自主、无源、可靠性强的优势,在卫星导航系统受到攻击等情况下可有效发挥替代作用,在军民用领域均具有极高的战略意义和应用价值。航空平台具有飞行速度快、短时间跨越地域广的特性,对地磁矢量测量与导航方法提出高精度和高可靠性等要求。该文梳理近年来航空地磁矢量导航系统的研究与发展现状,介绍地磁矢量导航的关键技术,重点对地磁矢量匹配导航算法的研究进展进行分析。针对现有算法存在的不足,提出进一步提升算法的精度和鲁棒性、发展基于机器学习的地磁矢量匹配导航方法、推动无人机等新型航空平台地磁矢量导航技术发展等后续研究方向,意在促进航空地磁矢量导航技术的进一步发展。

用于训练神经网络的自适应梯度下降优化算法

由于神经网络规模的扩大,模型训练变得越来越困难.为应对这一问题,提出了一种新的自适应优化算法——Adaboundinject.选取Adam的改进算法Adabound算法,引入动态学习率边界,实现了自适应算法向随机梯度下降(SGD)的平稳过渡.为了避免最小值的超调,减少在最小值附近的振荡,在Adabound的二阶矩中加入一阶矩,利用短期参数更新作为权重,以控制参数更新.为了验证算法性能,在凸环境下,通过理论证明了Adaboundinject具有收敛性.在非凸环境下,进行了多组实验,采用了不同的神经网络模型,通过与其他自适应算法对比,验证了该算法相比其他优化算法具有更好的性能.实验结果表明,Adaboundinject算法在深度学习优化领域具有重要的应用价值,能够有效提高模型训练的效率和精度.

基于弹性振动和深度学习的变压器状态识别

针对当前传统的变压器状态识别算法需要人工干预的问题,研究了一种能够自动提取特征并分类的一维卷积神经网络算法。该算法通过3层卷积池化层自动提取信号特征,并通过全连接层展为一维矢量,最终通过Softmax层进行分类。鉴于弹性振动信号抗干扰能力较强,选择弹性振动信号作为信号处理研究对象,运用基于一维卷积神经网络和弹性振动的方法对变压器状态进行识别,并通过采集500kV变压器的弹性振动信号获取的数据集进行验证,结果表明该算法的准确率优于BP、SVM和SAE算法,能对变压器的不同状态实现自动有效识别。

基于深度学习的逆变器电路图像数据智能识别方法

进行逆变器电路图像数据识别时,特征信息提取不充分使得无法准确捕捉到关键特征,导致识别精度下降。为此,提出一种基于深度学习的逆变器电路图像数据智能识别方法。首先,利用逆变器数据采集系统,采集逆变器电路图像数据。然后,将图像数据输入到卷积神经网络模型中,通过卷积核提取数据的特征。最后,采用YOLO算法对其进行有效识别,基于CA模块对特征信息进行关注,并利用Detect模块输出识别结果。Detect模块主要包括置信度函数和模型的损失函数,将两者结合,利用分类框和检测框来实现对逆变器电路图像的识别。实验结果表明,所提方法的识别误报率最高仅为6%,具有实用性。