Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching

The traditional Chinese-English translation model tends to translate some source words repeatedly,while mistakenly ignoring some words.Therefore,we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching.In this model,word vector,two-way LSTM,2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs.Self-organizing mapping(SOM)is used to classify and identify the sentence feature.The attention mechanism-based neural machine translation model is taken as the baseline system.The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.

Waterlogging risk assessment based on self-organizing map(SOM)artificial neural networks:a case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.

Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

Inverse lithography technology(ILT)is intended to achieve optimal mask design to print a lithography target for a given lithography process.Full chip implementation of rigorous inverse lithography remains a challenging task because of enormous computational resource requirements and long computational time.To achieve full chip ILT solution,attempts have been made by using machine learning techniques based on deep convolution neural network(DCNN).The reported input for such DCNN is the rasterized images of the lithography target;such pure geometrical input requires DCNN to possess considerable number of layers to learn the optical properties of the mask,the nonlinear imaging process,and the rigorous ILT algorithm as well.To alleviate the difficulties,we have proposed the physics based optimal feature vector design for machine learning ILT in our early report.Although physics based feature vector followed by feedforward neural network can provide the solution to machine learning ILT,the feature vector is long and it can consume considerable amount of memory resource in practical implementation.To improve the resource efficiency,we proposed a hybrid approach in this study by combining first few physics based feature maps with a specially designed DCNN structure to learn the rigorous ILT algorithm.Our results show that this approach can make machine learning ILT easy,fast and more accurate.

Study of TSP based on self-organizing map

Self-organizing map(SOM) proposed by Kohonen has obtained certain achievements in solving the traveling salesman problem(TSP).To improve Kohonen SOM,an effective initialization and parameter modification method is discussed to obtain a faster convergence rate and better solution.Therefore,a new improved self-organizing map(ISOM)algorithm is introduced and applied to four traveling salesman problem instances for experimental simulation,and then the result of ISOM is compared with those of four SOM algorithms:AVL,KL,KG and MSTSP.Using ISOM,the average error of four travelingsalesman problem instances is only 2.895 0%,which is greatly better than the other four algorithms:8.51%(AVL),6.147 5%(KL),6.555%(KG) and 3.420 9%(MSTSP).Finally,ISOM is applied to two practical problems:the Chinese 100 cities-TSP and102 counties-TSP in Shanxi Province,and the two optimal touring routes are provided to the tourists.

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.

Morphological self-organizing feature map neural network with applications to automatic target recognition

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Neural network-based matrix effect correction in EDXRF analysis

In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.

基于SOM-BP的全自动口罩机传动系统故障检测

针对口罩机在多工序生产中故障特征难以诊断的问题,提出了一种基于自组织映射(SOM)和误差反向传播网络(BP)的故障检测模型。首先针对4种减速机故障类型搭建SOM-BP复合型神经网络模型并完成检测分类,其次通过提取原振动信号的20组时域和频域参数作为SOM网络的输入样本进行初步聚类,并根据仿真结果确定最佳竞争层结构,最后将聚类后结果输入BP网络进行预测并完成分类,实现故障检测。研究结果表明,7×7竞争层结构下的SOM-BP复合型神经网络对于减速机的8种时域和频域参数的检测效果最优,分类准确率可达93.5%,173次迭代即可收敛,数据拟合度最高达0.99876,达到实际检测要求,验证了该方案的有效性和可行性。

无人机双目视觉鲁棒定位方法

无人机(unmanned aerial vehicle,UAV)在全球定位系统(global positioning system,GPS)信号拒止环境中的应用受到限制,传统视觉同步定位与建图(simultaneous localization and mapping,SLAM)技术一定程度上解决了该问题,但在动态场景和弱纹理场景中定位精度较差。针对该问题提出一种基于双目视觉的多场景鲁棒SLAM方法,重点考虑了真实环境中的动态和弱纹理2类具有挑战性的场景,利用双目相机为UAV在动态和弱纹理场景中提供位姿信息。针对动态场景利用掩膜基于区域的卷积神经网络(mask region-based convolutional neural network,Mask R-CNN)分割潜在动态内容并剔除动态特征,通过计算稠密光流同步相邻帧的掩膜,减小了掩膜的计算成本。对于弱纹理场景,在传统SLAM算法使用的点特征基础上融合了线特征,充分利用了环境中的结构特征。数值模拟和仿真实验证明了本文算法具有更高的鲁棒性和精确性。

人工智能深度学习模型在土壤属性数字制图中的应用

为提高土壤属性数字制图预测精度,以及随着遥感环境变量数据量的增加、算力的增强和开源深度学习框架的普及,数字土壤制图正在从传统的知识驱动模型向数据驱动的人工智能深度学习模型转变。该文以土壤关键属性有机碳为例,分析归纳土壤有机碳数字制图深度学习模型的理论基础、模型结构、亟待解决的有关环境变量空间上下文信息和多模态数据整合及模型可解释性等问题,旨在促进人工智能深度学习模型在第三次全国土壤普查土壤属性制图中的应用。

Pattern recognition of messily grown nanowire morphologies applying multi-layer connected self-organized feature maps

Multi-layer connected self-organizing feature maps(SOFMs) and the associated learning procedure were proposed to achieve efficient recognition and clustering of messily grown nanowire morphologies. The network is made up by several paratactic 2-D SOFMs with inter-layer connections. By means of Monte Carlo simulations, virtual morphologies were generated to be the training samples. With the unsupervised inner-layer and inter-layer learning, the neural network can cluster different morphologies of messily grown nanowires and build connections between the morphological microstructure and geometrical features of nanowires within. Then, the as-proposed networks were applied on recognitions and quantitative estimations of the experimental morphologies. Results show that the as-trained SOFMs are able to cluster the morphologies and recognize the average length and quantity of the messily grown nanowires within. The inter-layer connections between winning neurons on each competitive layer have significant influence on the relations between the microstructure of the morphology and physical parameters of the nanowires within.

基于自组织特征映射模型(SOFM)网络的中国自然资源生态安全区划

自然资源生态安全是国家安全的重要组成部分,自然资源生态安全区划对保障区域可持续发展提供了重要途径。基于自然资源数据、生态环境数据和相关区划资料,从生态敏感性与生态服务重要性角度构建了自然资源生态安全评价指标体系,进而揭示了中国自然资源生态安全的空间格局;通过建立区划的原则和指标,按照一级区主要反映自然资源空间分布格局,二级区主要揭示自然资源生态安全水平的差异,采用SOFM网络制订了中国自然资源生态安全区划方案。结果显示:(1)中国自然资源生态安全水平整体偏低,以中警与重警状态区域为主,安全和较安全状态的区域仅占24.22%,其中低安全等级区多分布于400mm等降水量线以西的干旱、半干旱区,高安全等级区则集中分布于水热资源与生物资源较为丰富的东南部地区;(2)中国自然资源生态安全区划方案包括8个一级区与27个二级区,总结归纳各大区自然资源的特征和威胁生态安全的问题,并针对二级区自然资源生态安全状况提出了对策建议。研究结果可为分区、分类推进全国自然资源可持续利用和国土空间优化提供理论支持与决策依据。

基于mRMR-SOM的异步电机轴承故障诊断研究

针对异步电机轴承故障诊断问题,提出了一种融合最大相关最小冗余特征选择算法(mRMR)和自组织映射神经网络(SOM)的故障诊断方法,并将其应用于轴承故障诊断的不同阶段。首先,在实验室环境下搭建了异步电机故障诊断试验平台,在不同电机状态下分别采集振动、电流和电压信号,利用统计学方法获取了高维混合特征集;然后,以互信息为背景,利用mRMR根据特征与状态标签间的相关性和特征间的冗余性,筛选了具备强区分能力的特征,以避免计算冗余和后验诊断性能下降;最后,采用SOM对异步电机健康和轴承故障状态进行了分类识别,验证了SOM对异步电机轴承故障诊断的有效性,以及mRMR对故障诊断结果的影响。研究结果表明:基于mRMR-SOM的异步电机轴承故障诊断方法能够准确地区分健康和故障状态,测试集分类准确率达到89%;使用mRMR特征筛选能够将154维特征降低至17维,缩短23.5%的网络收敛时间,并将分类准确率由89%提升至98%;试验结果验证了基于mRMR-SOM的异步电机轴承故障诊断方法对于异步电机轴承故障诊断问题的有效性,且证实其具备良好的诊断效果。

基于轻量级CNN的视觉SLAM快速回环检测算法

传统基于卷积神经网络(Convolutional Neural Network,CNN)的视觉同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)系统回环检测目前准确率和召回率较高,但其存在特征提取时间较长和特征向量维度过高导致计算量较大等问题。针对上述问题,结合轻量级卷积神经网络MobileNetV3和PCA降维算法,提出了一种基于深度学习的快速回环检测算法。基于MobileNetV3进行特征提取并构建特征矩阵,运用PCA降维算法完成降维以提升运行速度,使用余弦相似度计算各个特征向量间的相似性,并取最大值与给定阈值比较判断是否构成回环。最后,使用New College和City Centre两个公开的数据集验证算法的性能。实验结果表明,相较于传统的CNN回环检测方法,提出的算法在保证准确率和召回率的同时,运行速度更快,较好的满足了视觉SLAM系统准确性和实时性的要求。

基于时间序列和神经网络的电力设备状态异常检测方法

为进一步提高电力设备异常检测方法对设备信息的利用率,发现更多潜在的设备故障,结合大数据分析技术和设备评估技术,提出了一种基于时间序列和神经网络的状态数据异常检测方法。通过时间序列自回归模型和自组织映射神经网络将连续的电力设备数据离散为单个序列,计算状态变量在时间轴上的转移概率,通过状态转移概率和聚类算法快速检测数据异常。通过实验对该方法的有效性进行验证。结果表明,该方法可以快速、有效地检测电力设备异常状态。

基于混合自组织映射神经网络的云南省山洪灾害危险性区划

开展云南省山洪灾害危险性区划工作,以自组织映射神经网络为基础,混合Ward、PAM、CLARA、K-means和HK-means的5种方法进行二阶聚类,应用戴维森堡丁指数(Davies-Bouldin index,DBI)、轮廓系数(silhouette coefficient,SC)、聚类模型评估指数(Calinski-Harabaz index,CH)确定最佳聚类方案,之后以变异系数和变异系数一阶拆分确定最佳区划数量.结果显示:①SOM(self organizing map)+CLARA(clustering LARge applications)方法通过聚类有效性检验效果最好,其DBI值为1.0、SC值为0.9、CH值为0.3334,基于该方法得到云南省山洪灾害危险性最佳聚类数为5类,呈现类别空间分离,灾害属性相似的特征;②通过变异系数(coefficient of variation,CV)值变化及变异系数一阶差分(first-order difference,FOD)最低取值确定云南省山洪灾害危险性最佳区划单元为16个,具有形状上与地貌单元相近、数量上与行政单元相同,内部灾害发生机理相似的特征;③通过山洪灾害点、降水量、高程地貌的可视化比较,地理探测器定量分析,表明区划结果有较高的区内一致性和区间异质性.