强杂波中双波段目标检测新算法

对海空背景下的电视和3～5(m红外目标提出了一种采用多向梯度表决融合的检测方法:依据背景图像中点目标的奇异特性,得到图像的多向剃度检测结果,并进行表决融合.通过实测的电视和红外图像仿真,结果表明,采用\     

基于Quorum系统容错技术综述

Quorum系统是一种新型冗余拓扑的集合系统。在“冗余”设计的基础上,quorum通过交叉的结点把有效数据复制到其他quorum的结点中,增加了Quorum系统数据冗余性。当某些结点发生故障或者错误时,通过选举协议,从含有故障结点quorum的有效结点中选举出有效数据;或者采用互斥协议,从不含故障或者错误结点的有效quorum的结点中获得有效数据,系统仍能可靠运行。分析了各种Quorum肌系统的容错方式、性能比较,探讨了Quorum系统发展中需要改进的关键问题,并展望了未来的研究方向。     

基于voting集成的智能电能表故障多分类方法

为提升智能电能表故障准确分类能力,助力维护人员迅速排除故障,本文提出基于投票法voting集成的智能电能表故障多分类方法。首先针对实际智能电能表故障数据进行编码预处理,基于皮尔逊系数法筛选智能电能表故障分类关键影响因素,结合SMOTE算法解决数据类别不平衡问题,由此建立模型所需数据集,再通过投票法进行模型融合,结合粒子群PSO确定各基模型的权重,据此构建基于XGBT+KNN+NB模型的智能电能表故障多分类方法。实测实验结果表明：本文提出方法能有效实现智能电能表的故障快速准确分类,与现有方法相比,在智能电能表的故障分类精确率、召回率及F1-Score均有明显提升。     

Deep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm

In this study, new multiple deep classifiers with a modified Weighted Majority Voting (WMV)-based method are proposed to identify power quality disturbances (PQDs) in a hydrogen energy-based microgrid. In the proposed approach, closed-loop deep LSTM (Long Short Time Memory), deep CNN (Convolutional Neural Network), and hybrid (CNN-LSTM) models are used for automatic feature extraction and classification. Then, a modified WMV method is employed to ensemble the outputs of the three deep learning (DL) classifier models. The enhanced WMV mechanism performs an automatically updated weighting based on the validation results of the DL classification models, unlike voting methods in the literature. The improved WMV mechanism eliminates the challenges of using multiple DL classifiers in the voting system. The mathematical data results in LabVIEW, simulation results in Matlab/Simulink, and real data results in the laboratory show that the proposed method shows superior performance in accuracy and noise immunity to state-of-the-art methods.     

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.     

Decoupled deep hough voting for point cloud registration

Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud registration. Recently, a series of studies have attempted to combine traditional robust model fitting with deep learning. Among them, DHVR proposed a hough voting-based method, achieving new state-of-the-art performance. However, we find voting on rotation and translation simultaneously hinders achieving better performance. Therefore, we proposed a new hough voting-based method, which decouples rotation and translation space. Specifically, we first utilize hough voting and a neural network to estimate rotation. Then based on good initialization on rotation, we can easily obtain accurate rigid transformation. Extensive experiments on 3DMatch and 3DLoMatch datasets show that our method achieves comparable performances over the state-of-the-art methods. We further demonstrate the generalization of our method by experimenting on KITTI dataset.     

基于双层异质集成学习器的入侵检测方法

入侵检测是网络安全领域中具有挑战性和重要性的任务.现有研究以增加时间消耗和误报率为代价,重点关注如何提高检测率,在实际应用中代价较大.为此,本文提出了一种使用双层异质学习器集成学习策略的入侵检测IDHEL模型.该模型使用概率核主成分分析方法降低数据维度,采用多个异质分类器通过分层十折交叉验证策略进行异常检测,并根据所提...     

基于LSTM和多种异常定义的卫星异常检测方法

针对在轨卫星异常检测中现存的异常定义单一、检测流程不规范不灵活的问题,提出一种基于长短期记忆(Long-Short Term Memory,LSTM)网络和多种异常定义的卫星异常检测方法.基于某在轨卫星实测电源遥测数据,首先进行卫星时序数据预处理,随后以LSTM为示例算法对数据的"正常值"进行预测,最后分别以测试数据均值的标准差、预测结果均值的标准差和非参数动态阈值作为异常定义,进行异常的联合投票检测,检测流程可容纳丰富的预测算法和异常定义,且流程模块间耦合度低.仿真结果表明,LSTM模型预测结合多异常定义的联合投票机制能有效提升异常点检测的性能.     

An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier

Early detection of leukemia increases the chances of a speedier recovery. If a patient exhibits any symptoms, doctors would often examine a blood sample slide under a microscope to detect hematological malignancies. Manually categorizing leukocytes as normal or abnormal requires examining the many characteristics of the cells, which is time-consuming and error-prone. This research aims to create a transfer learning-based Acute Lymphocytic Leukemia (ALL) detection system that is both efficient and easy. To overcome the critical challenges associated with feature extraction, we used EfficientNet, the most recent and most substantial deep learning model. In this article, eight EfficientNets variations are used to extract features and are compared based on classification accuracy. This work uses an ensemble of three sophisticated classifiers, namely Support Vector Machine (SVM), Random Forest, and Logistic Regression, which achieves a classification accuracy of 98.5%.