Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

Alekseevskii-Tate revisited:An extension to the modified hydrodynamic theory of long rod penetration

The modified hydrodynamic theory of long rod penetration into semi-infinite targets was established independently by Alek-seevskii and Tate over forty years ago and since then many investigators contributed much to the development of the high speed penetration mechanics.However,in all the models proposed so far,the target resistance Rt is not well defined and usually determined by adjusting it until the predicted depth of penetration comes to an agreement with experimental data.In this paper,assumptions are...     

计算机编程语言与输入设备的发展问题初探

现代化建设事业的发展离不开计算机水平的提高。计算机编程语言作为人与机器沟通的桥梁,在计算机系统里有着非常重要的地位。本文从探讨计算机编程语言的概念出发,详细阐述了计算机编程语言所包含的内容和基本分类。接着笔者又深入分析了计算机输入设备的发展问题。最后,针对计算机编程语言和输入之间的联系问题,笔者做了观点性和理论性的论述分析。     

基于PCA-Logistic模型的围岩稳定性分类研究

为了建立易于理解和应用的围岩稳定性分类模型,采用主成分分析法对围岩样本数据进行降维处理,以Logistic模型来描述围岩稳定性影响因素与围岩稳定性之间的经验关系。选取影响围岩分类的5个主要因素,根据分类标准,采用在每级标准中随机内插的方法,得到100个标准样本,用于建立PCA-Logistic模型。分析结果显示,PCA-Logistic模型拟合效果良好,对工程实例的预测结果同投影寻踪模型和分形-插值模型完全一致,表明该模型在围岩稳定性评价中具有可行性和有效性。     

计算机内存的优化及管理浅析

信息总量、网络浏览人数以及人们对网络享受需求的增加,促使着信息制作水平的提高,制作水平的提高在给人们带来了更好的视觉和听觉享受的同时,也对计算机的硬件系统提出了巨大的考验.计算机内存作为计算机硬件系统的重要组成部分,能否对内存做好合理的划分,对计算机的体验具有重要的意义.     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

基于概念的形状分类与识别

基于内容的图像检索已经成为图像检索的一个重要研究方向。其中利用形状特征可以实现形状较为明显的各类图像库的检索。该文对图像形状特征的提取和描述方法进行了较深入地研究,选取其中一部分特征值,分别利用BP神经网络和支持向量机进行形状识别,取得令人满意的实验效果。     

Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.     

一种改进的应用于噪声数据中的KNN算法

基于实例的KNN算法不可避免地要依赖于数据的质量,但原始数据含有噪声,因而KNN算法的结果势必会因为数据中的噪声而受到严重的影响。事实上,大多噪声都服从一定的模型,而且模型一般是已知的。充分利用数据中的噪声模型,以减小噪声对KNN算法结果的影响。通过实验结果表明该方法是有效的。     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.