一种新的无监督的卫星影像变化检测算法

提出了一种基于主分量分析(PCA)和上下截集模糊Kohonen聚类网络(UDSFKCN)的、无监督的、不同时相的和卫星影像的像素级变化检测新算法.将PCA和UDSFKCN两种方法结合,并将它应用于不同时相的卫星影像的变化检测.该方法结合每个像素的邻域信息,利用PCA,产生每个像素对应的基于邻域信息的特征向量;又将变化区...     

Automatic transcription of continuous speech into syllable-like units for Indian languages

The focus of this paper is to automatically segment and label continuous speech signal into syllable-like units for Indian languages. In this approach, the continuous speech signal is first automatically segmented into syllable-like units using group delay based algorithm. Similar syllable segments are then grouped together using an unsupervised and incremental training (UIT) technique. Isolated style HMM models are generated for each of the clusters during training. During testing, the speech signal is segmented into syllable-like units which are then tested against the HMMs obtained during training. This results in a syllable recognition performance of 42·6% and 39·94% for Tamil and Telugu. A new feature extraction technique that uses features extracted from multiple frame sizes and frame rates during both training and testing is explored for the syllable recognition task. This results in a recognition performance of 48·7% and 45·36%, for Tamil and Telugu respectively. The performance of segmentation followed by labelling is superior to that of a flat start syllable recogniser (27·8%and 28·8%for Tamil and Telugu respectively).     

Gas-Chimney Detection in 3D Seismic by Neural Network

Recognition of hydrocarbon migration is so vital for petroleum exploration. Developing intelligent systems (artificial neural network) enable experts to achieve more details from seismic data. Although detection of migration direction using seismic data is difficult, Chimney-cube analysis overcomes this problem. The authors used several filters, seismic attributes, neural network (supervised and unsupervised), and interpreters' viewpoints. In supervised method artificial and human intelligence cover their limitations and in unsupervised method the authors eliminate the experts' views. Chimney recognizes the migration direction and locates the spill points, mud volcanoes, gas seepages, sealing, and nonsealing faults and finally the origin of hydrocarbon.     

无监督数据挖掘辅助激光诱导击穿光谱用于铜冶炼光谱结构解析

在有色冶炼领域,元素成分检测是保证冶炼质量的重要一环。目前国内有色冶炼企业多采用X射线荧光光谱法进行检测,该方法需要样品制备,造成冶炼状态无法实时反馈,严重影响冶炼过程优化。研究了无监督数据挖掘算法辅助激光诱导击穿光谱技术用于铜冶炼光谱结构解析。实验中,首先选择4种铜冶炼物料作为实验样品,然后利用激光诱导击穿光谱技术(LIBS)激发样品获得18750个光谱数据,通过盲源分离技术对所有光谱进行分析,最终提取得到3个特征光谱。进一步研究发现,3个特征光谱与Cu、Fe、Ca元素光谱有一一对应关系。在此基础上,提出了LIBS光谱的定量化评价指标,量化结果表明分解模型对18750个光谱都能达到很高的评分,说明铜冶炼光谱能够良好地被3个特征光谱重构,即铜冶炼光谱存在显著的光谱结构。以上结论在实际应用中具有重要研究价值,可用于光谱快速评价、异常光谱剔除、光谱信号提纯、元素谱线选取、样品定性/半定量分析等,为LIBS技术应用于在线铜冶炼成分分析奠定基础。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.     

基于聚类的快速支持向量机训练算法*

支持向量机(support vector machine, SVM)具有良好的泛化性能而被广泛应用于机器学习及模式识别领域。然而,当训练集较大时,训练SVM需要极大的时间及空间开销。另一方面,SVM训练所得的判定函数取决于支持向量,使用支持向量集取代训练样本集进行学习,可以在不影响结果分类器分类精度的同时缩短训练时间。采用混合方法来削减训练数据集,实现潜在支持向量的选择,从而降低SVM训练所需的时间及空间复杂度。实验结果表明,该算法在极大提高SVM训练速度的同时,基本维持了原始分类器的泛化性能。     

基于无监督学习的P2P流量识别

该文以应用层流量分类为重点,分析了流量分类中的一些关键问题;为了适应现代网络管理,实现对应用层流量的实时监控,针对现有方法存在的问题,提出了一种分层次的应用层流量识别方法.为了具有对新应用的发现能力,结合基于深层包检测和基于数据流特征的方法,并同时使用基于无监督学习技术,发现P2P等新的网络应用.通过对校园网流量的跟踪...     

含嵌入式直流的受端电网动态响应智能分析方法

直流输电系统嵌入大型交流系统，将进一步增加换相失败、直流闭锁等故障对系统安全稳定的影响，交流系统与直流系统的交互作用，将使系统动态更加复杂；针对电网不同运行方式下大扰动后系统复杂动态响应特性分析难题，本文提出一种基于机电-电磁混合仿真与机器学习的智能分析方法。该方法基于PCA降维、DBSCAN、Kmeans等算法建立二阶段聚类模型，可针对直流落点近区严重故障后大量混合仿真动态曲线在高维空间中自动聚类，并给出相应标识与严重程度，提取交直流系统在不同故障下的典型动态模式，并自动标注并识别各模式下主导安全稳定问题。本文所提方法的有效性在2025年华东电网运行方式中得以验证，仿真结果标明，所提方法可有效提取不同故障下系统动态模式，将有效支撑后续复杂故障下的交直流系统动态机理分析。     

A Fuzzy Hierarchical Classification System for Olfactory Signals

A fuzzy logic-based system to classify olfactory signals is presented. The odour samples are obtained from an electronic nose that contains conducting polymer sensors with partially overlapping sensitivities to odours. The sensor responses are represented by means of the coefficients of their Fast Fourier Transform (FFT). A feature reduction method is applied to reduce the feature space dimension. Then, an Unsupervised Fuzzy Divisive Hierarchical Clustering (UFDHC) method is used to establish the optimal number of clusters in the data set, as well as the optimal cluster structure. The output of UFDHC is a binary hierarchy of fuzzy classes that are adopted to build a supervised fuzzy hierarchical classifier. At each level of the hierarchy a separating hyperplane of the two corresponding fuzzy classes is determined. The hyperplane identifies two crisp decision regions, which will be refined at the next level of the hierarchy. In this way, we obtain a hierarchy of regions, which defines a crisp decision tree. Each region is, therefore, related to a specific expected output of the system. Two small-scale applications demonstrate the effectiveness and the good recognition performance of the proposed method.     

A brainlike learning system with supervised,unsupervised, and reinforcement learning

According to Hebb's cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that in the brain there are three different learning paradigms: supervised, unsupervised, and reinforcement learning, which are related deeply to the three parts of brain: cerebellum, cerebral cortex, and basal ganglia, respectively. Inspired by the above knowledge of the brain in this paper we present a brainlike learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part, and reinforcement learning (RL) part. The SL part is a main part learning input–output mapping; the UL part is a competitive network dividing input space into subspaces and realizes the capability of function localization by controlling firing strength of neurons in the SL part based on input patterns; the RL part is a reinforcement learning scheme, which optimizes system performance by adjusting the parameters in the UL part. Numerical simulations have been carried out and the simulation results confirm the effectiveness of the proposed brainlike learning system. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 32–39, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20600