一个基于Java的可视化挖掘工具的设计和实现

介绍了一个可视化挖掘工具,给出了其框架体系结构,并主要从数据挖掘技术、数据视图及其操纵界面技术、系统安全性三个方面作了介绍。     

Acoustic environment as an indicator of social and physical context

Acoustic environments provide many valuable cues for context-aware computing applications. From the acoustic environment we can infer the types of activity, communication modes and other actors involved in the activity. Environmental or background noise can be classified with a high degree of accuracy using recordings from microphones commonly found in PDAs and other consumer devices. We describe an acoustic environment recognition system incorporating an adaptive learning mechanism and its use in a noise tracker. We show how this information is exploited in a mobile context framework. To illustrate our approach we describe a context-aware multimodal weather forecasting service, which accepts spoken or written queries and presents forecast information in several forms, including email, voice and sign languages.

Audio indexing: primary components retrieval

This work addresses the soundtrack indexing of multimedia documents. Our purpose is to detect and locate sound unity to structure the audio dataflow in program broadcasts (reports). We present two audio classification tools that we have developed. The first one, a speech music classification tool, is based on three original features: entropy modulation, stationary segment duration (with a Forward–Backward Divergence algorithm) and number of segments. They are merged with the classical 4 Hz modulation energy. It is divided into two classifications (speech/non-speech and music/non-music) and provides more than 90% of accuracy for speech detection and 89% for music detection. The other system, a jingle identification tool, uses an Euclidean distance in the spectral domain to index the audio data flow. Results show that is efficient: among 132 jingles to recognize, we have detected 130. Systems are tested on TV and radio corpora (more than 10 h). They are simple, robust and can be improved on every corpus without training or adaptation.

Convergence Results of One-Leg and Linear Multistep Methods for Multiply Stiff Singular Perturbation Problems

One-leg methods and linear multistep methods are two class of important numerical methods applied to stiff initial value problems of ordinary differential equations. The purpose of this paper is to present some convergence results of A-stable one-leg and linear multistep methods for one-parameter multiply stiff singular perturbation problems and their corresponding reduced problems which are a class of stiff differential-algebraic equations. Received April 14, 2000; revised June 30, 2000     

Effect of neighbouring village conditions and infrastructure interdependency on economic opportunity: A case study of the Yogyakarta region,Indonesia

To determine whether the inclusion of conditions in neighbouring villages and infrastructure interdependency are able to improve the performance of infrastructure–economy interaction models, we compare three related and progressive concepts. The first concept defines economic opportunity in a village as a function of available infrastructure within that village. The second concept includes the effect of economic opportunity in neighbouring villages in addition to available infrastructure within the village. In the third concept, we include the interdependency of infrastructures as another factor affecting the potential level of economic development in the village. We use Ordinary Least Squares (OLS) and a Geographic Information System (GIS) to model the first concept, and we add a spatial-lag model for the second. The third model expands on the second by introducing a Sugeno Fuzzy Inference System (FIS) and a rule-based OLS to capture the nature of infrastructure interdependency. The result of the three models is validated by a known spatial distribution of poverty levels and subjective well-being that serve as proxies for economic opportunity in the Yogyakarta region in Indonesia. The results demonstrate that the third model provides a more accurate prediction of the real conditions and performs consistently better than the other two models. We therefore conclude that conditions in neighbouring villages and infrastructure interdependency influence the economic opportunity of a village and should be considered in policy making regarding resource allocation in infrastructure development.     

Parameter estimation of q-Gaussian Radial Basis Functions Neural Networks with a Hybrid Algorithm for binary classification

A classification problem is a decision-making task that many researchers have studied. A number of techniques have been proposed to perform binary classification. Neural networks are one of the artificial intelligence techniques that has had the most successful results when applied to this problem. Our proposal is the use of q-Gaussian Radial Basis Function Neural Networks (q-Gaussian RBFNNs). This basis function includes a supplementary degree of freedom in order to adapt the model to the distribution of data. A Hybrid Algorithm (HA) is used to search for a suitable architecture for the q-Gaussian RBFNN. The use of this type of more flexible kernel could greatly improve the discriminative power of RBFNNs. In order to test performance, the RBFNN with the q-Gaussian basis functions is compared to RBFNNs with Gaussian, Cauchy and Inverse Multiquadratic RBFs, and to other recent neural networks approaches. An experimental study is presented on 11 binary-classification datasets taken from the UCI repository. Moreover, aerial imagery taken in mid-May, mid-June and mid-July was used to evaluate the potential of the methodology proposed for discriminating Ridolfia segetum patches (one of the most dominant and harmful weeds in sunflower crops) in two naturally infested fields in southern Spain.     

An ontology of slums for image-based classification

Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.     

基于EMD的电能质量扰动检测与分类方法

针对电能质量扰动信号的检测和分类问题,提出了一种基于EMD的电能质量扰动检测与分类方法,并对常见的几种扰动信号进行仿真。应用该方法检测扰动发生时间、持续时间以及扰动的频率与幅值。仿真结果表明,所提算法简洁有效,能够准确分析电能质量扰动信号,可以估计电压凹陷、电压凸起以及电压中断等扰动信号的变化幅度和频率。     

岩体工程分类方法及应用研究

工程岩体分类在工程建设中起着重要的作用。近年来,国内外专家通常采用定性和定量相结合的方法来评价岩体的工程性质,并根据其工程类型和使用目的对工程岩体进行分类。本文主要通过实例来介绍RMR和Q分类系统,并分析两类分类系统的相互关系。     

丽江西林瓦村纳西聚落美之要素研究

本文以云南丽江西林瓦村纳西聚落为研究对象。通过实地调研、案例分析、归纳总结等方法,分析其选址生聚、街道脉络、院落秩序、建筑造型、细部涵义诸方面对于生态之美、形式之美、生活之美的积极作用,提出院落组织的逻辑有序,因势相宜是纳西聚落之美的核心,是进一步研究和发扬的岀发点.