Mammogram Classification with HanmanNets Using Hanman Transform Classifier

Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.

基于改进Transformer的复合故障解耦诊断方法

大多复合故障诊断方法视复合故障为一种新的单一故障类型,忽视了内部单一故障的相互作用、故障分析粒度含糊和解释性差.为了解决复合故障难解耦的问题,针对工业环境中复合故障数据极少的情况,提出一种基于改进Transformer的复合故障解耦诊断方法.诊断流程分为预处理、特征提取和故障解耦3个步骤.故障解耦引入Transformer的解码器,利用交叉注意力机制使得每个单一故障标签可以在提取的特征层中,自适应地关注到与故障特征相对应的判别特征区域,进一步预测每个单一故障标签的输出概率以实现复合故障解耦.设计多组复合故障试验与业界先进算法进行对比,以验证方法的有效性.结果表明,所提方法在少量单一故障训练样本和极少量复合故障训练样本情况下,有较高的诊断准确度.当训练集中复合故障样本数仅为5时,复合故障诊断准确度达到88.29%,与其他方法比较更具有显著优势.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

The imbalance of dissolved gas analysis(DGA)data will lead to over-fitting,weak generalization and poor recognition performance for fault diagnosis models based on deep learning.To handle this problem,a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network(ACGAN)under imbalanced data is proposed in this paper,which meets both the requirements of balancing DGA data and supplying accurate diagnosis results.The generator combines one-dimensional convolutional neural networks(1D-CNN)and long short-term memories(LSTM),which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.The discriminator adopts multilayer perceptron networks(MLP),which prevents the discriminator from losing important features of DGA data when the network is too complex and the number of layers is too large.The experimental results suggest that the presented approach can effectively improve the adverse effects of DGA data imbalance on the deep learning models,enhance fault diagnosis performance and supply desirable diagnosis accuracy up to 99.46%.Furthermore,the comparison results indicate the fault diagnosis performance of the proposed approach is superior to that of other conventional methods.Therefore,the method presented in this study has excellent and reliable fault diagnosis performance for various unbalanced datasets.In addition,the proposed approach can also solve the problems of insufficient and imbalanced fault data in other practical application fields.

基于深度学习的电机故障诊断

故障诊断在保证电机的稳定运行中占据着非常重要的地位,因此,故障诊断在当前的研究中是一个热点。该研究利用短时傅里叶变换把一维的振动信号转换成二维的时频图,进而解决电机轴承的振动信号的非线性和不稳定性问题,并且作为卷积神经网络的输入,通过对故障特征信号的直接提取,来形成样本数据集,通过卷积神经网络与softmax多分类器来建立故障诊断模型,在Python中验证该算法优化的准确性,证明了该算法可以提高电机故障诊断的准确率。

小波域在无线局域网络信号增强中的应用

为提升无线局域网络信号增强中的去噪效果,应用小波域思想,设计一种无线局域网络信号增强算法.针对无线局域网络,收集无线局域网络原始功率谱数据,通过功率谱拟合因子提取信号特征,识别网络信号.对于识别的无线局域网络信号,通过过零率和短时功率提取该信号.基于小波域对无线局域网络信号实施去噪处理,分为二维小波变换、二进剖分、信号重构三个步骤.通过贝叶斯方法,在实施稀疏字典训练的同时,实现无线局域网络信号的增强处理,在训练中结合K-SVD算法,将信号增强过程和稀疏字典学习过程进行迭代和融合.将MATLAB R2019a作为测试设计算法的实验平台,利用计算机开展算法性能测试.测试结果表明:设计算法的无线局域网络信号增强性能良好,同时信号去噪性能较强,说明算法满足设计需求,在完善细节后可以投入实际应用.

基于SVDD和改进K-Means的变压器故障诊断模型

变压器状态对于智能配电房的安全稳定运行具有重要意义。为实现对变压器故障的准确诊断,在变压器油中溶解气体分析(DGA)的基础上,提出了一种联合使用支持向量数据描述(SVDD)和改进K-Means聚类的变压器故障诊断方法。首先利用SVDD构造闭合分类曲面实现“正常”和“故障”两类判断,然后对“故障”类样本进行K-Means聚类分析,自动将其划分为低能放电、中低温过热、高能放电、高温过热和局部放电5种故障类型,同时针对K-Means初始聚类中心选取难题,提出局部密度概念自动确定K-Means初始聚类中心,提升聚类性能。最后利用变压器故障真实数据开展实验,结果表明,相较于支持向量机(SVM)和BP神经网络模型,所提方法的故障诊断准确率分别提升9.8%和8%。

Large Power Transformer Fault Diagnosis and Prognostic Based on DBNC and D-S Evidence Theory

Power transformer is a core equipment of power system, which undertakes the important functions of power transmission and transformation, and its safe and stable operation has great significance to the normal operation of the whole power system. Due to the complex structure of the transformer, the use of single information for condition-based maintenance (CBM) has certain limitations, with the help of advanced sensor monitoring and information fusion technology, multi-source information is applied to the prognostic and health management (PHM) of power transformer, which is an important way to realize the CBM of power transformer. This paper presents a method which combine deep belief network classifier (DBNC) and D-S evidence theory, and it is applied to the PHM of the large power transformer. The experimental results show that the proposed method has a high correct rate of fault diagnosis for the power transformer with a large number of multi-source data.

Quasi-LFM radar waveform recognition based on fractional Fourier transform and time-frequency analysis

Recent advances in electronics have increased the complexity of radar signal modulation.The quasi-linear frequency modulation(quasi-LFM)radar waveforms(LFM,Frank code,P1−P4 code)have similar time-frequency distributions,and it is difficult to identify such signals using traditional time-frequency analysis methods.To solve this problem,this paper proposes an algorithm for automatic recognition of quasi-LFM radar waveforms based on fractional Fourier transform and time-frequency analysis.First of all,fractional Fourier transform and the Wigner-Ville distribution(WVD)are used to determine the number of main ridgelines and the tilt angle of the target component in WVD.Next,the standard deviation of the target component's width in the signal's WVD is calculated.Finally,an assembled classifier using neural network is built to recognize different waveforms by automatically combining the three features.Simulation results show that the overall recognition rate of the proposed algorithm reaches 94.17%under 0 dB.When the training data set and the test data set are mixed with noise,the recognition rate reaches 89.93%.The best recognition accuracy is achieved when the size of the training set is taken as 400.The algorithm complexity can meet the requirements of real-time recognition.

Shape classification based on singular value decomposition transform

In this paper, a new shape classification system based on singular value decomposition (SVD) transform using nearest neighbour classifier was proposed. The gray scale image of the shape object was converted into a black and white image. The squared Euclidean distance transform on binary image was applied to extract the boundary image of the shape. SVD transform features were extracted from the the boundary of the object shapes. In this paper, the proposed classification system based on SVD transform feature extraction method was compared with classifier based on moment invariants using nearest neighbour classifier. The experimental results showed the advantage of our proposed classification system.

An Efficient Framework for Indian Sign Language Recognition Using Wavelet Transform

Hand gesture recognition system is considered as a way for more intuitive and proficient human computer interaction tool. The range of applications includes virtual prototyping, sign language analysis and medical training. In this paper, an efficient Indian Sign Language Recognition System (ISLR) is proposed for deaf and dump people using hand gesture images. The proposed ISLR system is considered as a pattern recognition technique that has two important modules: feature extraction and classification. The joint use of Discrete Wavelet Transform (DWT) based feature extraction and nearest neighbour classifier is used to recognize the sign language. The experimental results show that the proposed hand gesture recognition system achieves maximum 99.23% classification accuracy while using cosine distance classifier.

基于中间层频域特征蒸馏的元学习算法

针对目前知识蒸馏忽略特征图全局统计信息的问题,提出利用离散余弦变换(DCT)频域释义的全局统计特征知识,并对各类别的频域特征使用Logistic分类器进行二分类,提取类间差异性信息.利用元学习算法(MPL)对教师模型参数进行更新,使教师模型能动态调整所传递的频域特征.实验模型在CIFAR-10,CIFAR-100以及ImageNet 2012数据集上有0.12%~0.16%的精度提升,结果表明,频域特征与类间相似性信息为学生模型的训练提供更多有用的知识,且两模型的知识交互更有利于教师的知识迁移.

Observation points classifier ensemble for high-dimensional imbalanced classification

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

基于图像特征转换和级联分类器的混凝土表面裂缝快速识别方法

提出了一种基于图像特征转换和级联分类器模型的快速混凝土表面裂缝快速识别方法,并利用交叉验证法对网络结构进行了优化。研究结果表明:将裂缝图案经特征变换后转化为一组特征值,能够有效突出裂缝的形态特征,极大降低运算复杂性;级联分类器模型对样本库容量要求更低,效率较高,具有更高的鲁棒性及识别准确度。

基于多源判据的变电站二次设备故障自动化诊断研究

为了解决变电站二次设备故障诊断效率低、精度差的问题,该文提出一种基于多源判据的变电站二次设备故障自动化诊断方法,建立变电站二次设备体系结构模型,分析产生故障的根本原因;采用支持向量机方法分类电力二次设备数据组,通过主分量分析技术对电力信号降维;结合阻抗值、电流、电压多源征兆测量阻抗判据值,通过阻抗判断值诊断电路中是否有故障,实现变电站二次设备故障自动化诊断。实验结果表明,所提方法可以诊断出设备气体泄漏故障、绝缘材料腐蚀故障、电弧重燃故障,且故障诊断准确率在85%以上。

沁心选煤厂SSC700分级破碎机齿辊改造升级

针对沁心选煤厂1台SSC700原煤分级破碎机使用过程中出现破碎齿辊磨损严重、齿环齿头断裂、齿辊窜轴的问题,结合分级破碎机齿辊的整体结构和外观尺寸,采取更换齿型结构、优化齿环锁紧方式的措施对破碎齿辊进行改造升级。生产实践表明,改造后的齿辊具有耐磨性能好、破碎效率高、可靠性能高的特点,在实际生产中取得了较好的效果。

3245格子型球磨机进料端改造及实践

龙桥矿业为了在格子型球磨机与螺旋分级机组成的一段磨矿分级系统增加磨前预选工艺,针对格子型球磨机螺旋给料器故障率较高、螺旋分级机分级效率差、系统处理能力较低,会导致改造后磨前预选系统不能达到设计处理能力的问题,进行了一段磨矿分级系统改造研究。通过拆除球磨机传统螺旋给料器,利用弯管给料、喂料,降低了设备故障率;利用旋流器代替双螺旋分级机,提高了分级效率,使生产流程顺畅并达到了设计处理能力。改造后年可节约采购、消耗备件费用20万元以上,年可节约动力成本10万元以上,经济效益显著。

融合SIFT和级联分类器的特种车辆自动检测识别

针对特定场景中特种车辆因多环境影响因素下数据不均衡、检测精度和识别准确率低的问题,提出一种融合尺度不变特征变换(Scale-Invariant Feature Transform,SIFT)和级联分类器的特种车辆自动检测及识别预测方法。首先,图像预处理后运用SIFT特征提取图像主体区域特征点及特征描述子;其次,结合SIFT特征点的密度调整优化算法实现目标车辆检测;最后,运用KMeans聚类算法获得目标检测框中SIFT特征描述子的中心聚类点,生成表征目标主体图像的128维特征描述子,并最终输入RF-RBF(Random Forest-Radial Basis Function)级联分类器进行学习并识别预测。该文均采用K折交叉验证方法保证模型的稳定性和可靠性。实验结果表明,在特定场景下特种车辆目标检测获得了75.47%平均交并比,级联分类器在特种车辆识别的综合准确率、精确率、召回率、F1-Score值以及FPS值分别为87.35%、88.17%、97.27%、92.38%以及21。进一步验证融合SIFT和级联分类器模型具有较好的自动化检测准确性和识别分类能力。

基于线性分类器的充油变压器潜伏性故障诊断方法

油中溶解气体分析(DGA)是判别变压器内部绝缘状况及发现内部潜伏性故障的重要手段。文中介绍了一种基于线性分类器、以DGA数据为特征参数的充油变压器潜伏性故障的识别方法。运用该方法进行了大量的应用实例分析,并将识别结果与BP神经网络法以及IEC三比值法进行了对比。结果表明选用H_2、CH_4、C_2H_2、C_2H_4、C_2H_6、CO、CO_2七种特征气体作为特征参数时,该方法显示出较高的准确度。

基于多级支持向量机分类器的电力变压器故障识别

支持向量机是以统计学习理论为基础发展起来的新的通用学习方法 ,较好地解决了小样本、高维数、非线性等学习问题。提出了一种基于多级支持向量机分类器的电力变压器故障识别方法。该方法首先通过特殊数值处理过程 ,对色谱分析法检测到的特征气体含量进行数值预处理 ,提取出故障识别所需要的 6个特征量 ,然后利用数值预处理后得到的数据样本分别对三级支持向量机进行训练和识别 ,并最后判断输出变压器所处的状态。测试结果表明 ,该方法具有三个优点 :1 )具有较强的鲁棒性 ,识别正确率极高 ;2 )训练时间很短 ,实时性能好 ;3 )不存在局部极小问题。