Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

Clustering analysis is one of the main concerns in data mining.A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other.Therefore,measuring the distance between sample points is crucial to the effectiveness of clustering.Filtering features by label information and mea-suring the distance between samples by these features is a common supervised learning method to reconstruct distance metric.However,in many application scenarios,it is very expensive to obtain a large number of labeled samples.In this paper,to solve the clustering problem in the few supervised sample and high data dimensionality scenarios,a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information,such as Must-Link and Cannot-Link,and then cluster the data in the new metric space.The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping.Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm.Average clustering metrics on various datasets improved by 8%compared to the comparison algorithm.

IMIBSE与ISOMAP在旋转机械故障诊断中的应用

针对基本熵的区域划分标准不理想,导致无法有效测量振动信号的复杂度,使故障诊断的准确率不佳这一问题,提出了一种基于改进多尺度改进基本熵(IMIBSE)、等距特征映射(ISOMAP)和随机森林(RF)的旋转机械故障诊断方法。首先,采用基于方差的区域划分准则对基本熵进行了改进,结合改进的粗粒化处理,提出了IMIBSE,并将其用于提取故障特征;随后,利用ISOMAP对原始故障特征进行了特征降维,选择了对分类贡献最大的一组特征作为故障敏感特征;最后,基于RF建立了多故障分类器,将故障敏感特征输入至RF模型进行了训练和测试,实现了旋转机械的故障识别,利用齿轮箱和离心泵两种故障数据集将IMIBSE方法与复合多尺度基本熵、多尺度改进基本熵、多尺度基本熵进行了比较和分析。研究结果表明:IMIBSE不仅具有最佳的可视化效果,而且取得的识别准确率最高,二者均达到了100%,而二者的平均分类准确率分别为100%和99.8%;相较于其他故障诊断方法,IMIBSE方法的准确率更高,而且适用于小样本的故障识别问题。

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

CLUSTERING PROPERTIES OF FUZZY KOHONEN'S SELF-ORGANIZING FEATURE MAPS

A new clustering algorithm called fuzzy self-organizing feature maps is introduced. It can process not only the exact digital inputs, but also the inexact or fuzzy non-digital inputs, such as natural language inputs. Simulation results show that the new algorithm is superior to original Kohonen’s algorithm in clustering performance and learning rate.

A Topology Mapping Method for Feature Extraction of Irregular Curve Shape

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Cytogenetic Mapping of Disease Resistance Genes and Analysis of Their Distribution Features on Chromosomes in Maize

Cytogenetic maps of four clusters of disease resistance genes were generated by ISH of the two RFLP markers tightly linked to and flanking each of maize resistance genes and the cloned resistance genes from other plant species onto maize chromosomes, combining with data published before. These genes include Helminthosporium turcium Pass resistance genes Ht1, Htn1 and Ht2, Helminthosporium maydis Nisik resistance genes Rhm1 and Rhm2, maize dwarf mosaic virus resistance gene Mdm1, wheat streak mosaic virus resistance gene Wsm1, Helminthosporium carbonum ULLstrup resistance gene Hml and the cloned Xanthomonas oryzae pv. Oryzae resistance gene Xa21 of rice, Cladosporium fulvum resistance genes Cf-9 and Cf-2.1 of tomato,and Pseudomonas syringae resistance gene RPS2 of Arabidopsis. Most of the tested disease resistance genes located on the four chromosomes, i.e., chromosomes1, 3, 6 and 8, and they closely distributed at the interstitial regions of these chromosomal long arms with percentage distances ranging 31.44(±3.72)-72.40(±3.25) except for genes Rhm1, Rhm2, Mdm1 and Wsm1 which mapped on the satellites of the short arms of chromosome6. It showed that the tested RFLP markers and genes were duplicated or triplicated in maize genome. Homology and conservation of disease resistance genes among species, and relationship between distribution features and functions of the genes were discussed. The results provide important scientific basis for deeply understanding structure and function of disease resistance genes and breeding in maize.

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.

Evaluation of effective spectral features for glacial lake mapping by using Landsat-8 OLI imagery

Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province,China

Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.

Blank Panel Design of Integral Wing Skin Panels Based on Feature Mapping Methods

A blank panel design algorithm based on feature mapping methods for integral wing skin panels with supercritical airfoil surface is presented.The model of a wing panel is decomposed into features,and features of the panel are decomposed into information of location,direction,dimension and Boolean types.Features are mapped into the plane through optimal surface development algorithm.The plane panel is modeled by rebuilding the mapped features.Blanks of shot-peen forming panels are designed to identify the effectiveness of the methods.

An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Configurable ontology mapping based on multi-feature

A configurable ontology mapping approach based on different kinds of concept feature information is introduced in this paper. In this approach, ontology concept feature information is classified as five kinds, which respectively corresponds to five kinds of concept similarity computation methods. Many existing ontology mapping approaches have adopted the multi-feature reasoning, whereas not all feature information can be computed in the real ontology mapping and only fractional feature information needs to be selected in the mapping computation. Consequently a configurable ontology mapping model is introduced, which is composed of CMT model, SMT model and related transformation model. Through the configurable model, users can conveniently select the most suitable features and configure the suitable weights. Simultaneously, a related 3-step ontology mapping approach is also introduced. Associated with the traditional name and instance learner-based ontology mapping approach, this approach is evaluated by an ontology mapping application example.

磁共振T_(2)^(*)mapping序列联合纹理特征参数对绝经后骨质疏松症的诊断价值

目的:探讨磁共振T_(2)^(*)mapping序列联合纹理特征参数在诊断女性绝经后骨质疏松症(postmenopausal osteoporosis,PMOP)中的应用价值。方法:回顾性分析2019年3月至2021年4月某院收治的50例PMOP患者(作为骨质疏松组)的临床资料,另选择50例绝经后骨量正常(骨密度>120 mg/cm^(3))的体检者为骨量正常组和50例绝经后骨量减少(80 mg/cm^(3)<骨密度<120 mg/cm^(3))者为骨量减少组。3组均行磁共振腰椎常规序列及T_(2)^(*)mapping扫描,提取图像纹理特征。对比3组腰椎T_(2)^(*)值及纹理特征参数(能量、对比、相关、逆差距及熵),采用Spearman相关性分析腰椎T_(2)^(*)值、纹理特征参数与女性发生POMP的相关性,通过多因素Logistics回归分析确定女性发生PMOP的独立预测因子,采用ROC曲线分析T_(2)^(*)mapping序列联合纹理特征参数对PMOP的诊断效能,构建发生POMP的列线图预测模型并对模型进行验证。结果:3组腰椎T_(2)^(*)值、能量、对比、相关、逆差距及熵比较,差异有统计学意义(P<0.05)。Spearman相关分析结果显示,腰椎T_(2)^(*)值、对比及熵与发生POMP呈显著正相关关系,能量、相关及逆差距与发生POMP呈显著负相关关系(P<0.05)。T_(2)^(*)值、能量、对比、相关、逆差距及熵均为POMP发生的独立预测因子(P<0.05)。T_(2)^(*)mapping序列联合纹理特征参数诊断的AUC、敏感度及特异度均优于单项参数诊断。列线图预测模型预测女性发生POMP的概率为75.00%,经验证模型具有较高的区分度、校准能力和净获益率。结论:磁共振T_(2)^(*)mapping序列联合纹理特征参数对女性发生POMP的诊断效能良好,T_(2)^(*)值、能量、对比、相关、逆差距及熵可作为临床筛选POMP高危人群的敏感指标。

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

Manufacturing Oriented Fuzzy Function-Feature Mapping

The mechanism of the Concurrent Engineering(CE)and the methods of the manufacturing consultation are discussed. CE-oriented computer aided manufacture consulting methods are one of the key issues in concurrent design. The problems are settled in part function-feature mapping in concurrent design. The fuzzy set theory is applied to the function-feature mapping. A method for part function-feature mapping based on fuzzy theory is presented.

基于Geoway Feature的基础测绘变化信息检测研究

随着各领域对基础地理信息现势性需求的增强,基础测绘工作需实现变化要素的实时处理,因此变化信息检测的效率与精度成为制约基础测绘更新效率的瓶颈。以提高变化信息提取的自动化程度、促进靶向性更新的快速开展为目标,依托Geoway Feature遥感信息提取软件,采用基于深度学习策略的计算机变化信息检测方法,对甘肃省庆城市区域多时相卫星正射影像展开试验,最后对实验效能予以评价,对其应用前景、研究方向进行了展望。结果表明Geoway Feature软件的检测结果的准确性与检测对象种类、样本库数量质量相关性大。

Feature Mapping and Recuperation by Using Elliptical Basis Function Networks for Robust Speaker Verification

The performance of speaker verification systems is often compromised under real world environments. For example, variations in handset characteristics could cause severe performance degradation. This paper presents a novel method to overcome this problem by using a non linear handset mapper. Under this method, a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs. During feature recuperation, clean features are recovered by feeding the distorted features to the feature mapper. The recovered features are then presented to a speaker model as if they were derived from clean speech. Experimental evaluations based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.

Feature Modeling and Variability Modeling Syntactic Notation Comparison and Mapping

Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.