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.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

A general topology-based framework for adaptive insertion of cohesive elements in finite element meshes

Large-scale simulation of separation phenomena in solids such as fracture, branching, and fragmentation requires a scalable data structure representation of the evolving model. Modeling of such phenomena can be successfully accomplished by means of cohesive models of fracture, which are versatile and effective tools for computational analysis. A common approach to insert cohesive elements in finite element meshes consists of adding discrete special interfaces (cohesive elements) between bulk elements. The insertion of cohesive elements along bulk element interfaces for fragmentation simulation imposes changes in the topology of the mesh. This paper presents a unified topology-based framework for supporting adaptive fragmentation simulations, being able to handle two- and three-dimensional models, with finite elements of any order. We represent the finite element model using a compact and “complete” topological data structure, which is capable of retrieving all adjacency relationships needed for the simulation. Moreover, we introduce a new topology-based algorithm that systematically classifies fractured facets (i.e., facets along which fracture has occurred). The algorithm follows a set of procedures that consistently perform all the topological changes needed to update the model. The proposed topology-based framework is general and ensures that the model representation remains always valid during fragmentation, even when very complex crack patterns are involved. The framework correctness and efficiency are illustrated by arbitrary insertion of cohesive elements in various finite element meshes of self-similar geometries, including both two- and three-dimensional models. These computational tests clearly show linear scaling in time, which is a key feature of the present data-structure representation. The effectiveness of the proposed approach is also demonstrated by dynamic fracture analysis through finite element simulations of actual engineering problems.

Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

If the production process, production equipment, or material changes, it becomes necessary to execute pilot runs before mass production in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Although, artificial neural networks are widely utilized to extract management knowledge from acquired data, sufficient training data is the fundamental assumption. Unfortunately, this is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize bootstrap to generate virtual samples to fill the information gaps of sparse data. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set.     

基于电子邮件的数据交换机制的设计与实现

目前很多实际软件项目需求中都要求多个网络主机互相自动传输数据文件,而目前已有的解决方案均不能很好地满足此要求。该文首先分析了传统方案的若干缺陷,并针对性地提出一种全新的基于电子邮件的数据交换传输机制,并详细介绍了这种新传输机制的总体思路和过程,最后分析了这种新传输机制的优势。     

民政低保系统中基于XML的数据交换浅析

该文主要介绍电子政务在我国的发展,低保信息系统概述,重点阐述低保信息系统中面临的数据交换问题,并对基于XML的数据交换平台的构建进行分析。     

关联规则剪枝的运用

该文重点介绍了最新的一种关联规则后处理的方法,并且我们提出了这种方法的优化算法,能够有效去除关联规则集合中的无趣模式,并且为模式的可视化提供了良好的工具。相关实验表明该方法具有更好的模式后处理能力。     

图像欧氏距离在人脸识别中的应用研究

图像欧氏距离可以嵌入到许多传统的图像分类识别算法中,该嵌入是通过对原始图像的线性变换来实现的,给出了一种基于数据场的图像线性变换方法,将其应用到图像欧氏距离中.实验结果表明,基于数据场的线性变换方法是一种可行的图像线性变换方法,该方法可以完成大尺度图像的线性变换,方便地将图像欧氏距离嵌入到传统人脸识别算法中.     

A data collection protocol for real-time sensor applications

The nature of many sensor applications as well as continuously changing sensor data often imposes real-time requirements on wireless sensor network protocols. Due to numerous design constraints, such as limited bandwidth, memory and energy of sensor platforms, and packet collisions that can potentially lead to an unbounded number of retransmissions, timeliness techniques designed for real-time systems and real-time databases cannot be applied directly to wireless sensor networks. Our objective is to design a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as a graph coloring problem. We then present TIGRA (Timely Sensor Data Collection using Distributed Graph Coloring) — a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node. Although the end-to-end delay incurred by sensor data collection largely depends on a specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees a deterministic delay on sensor data collection.     

The effect of time of day on injury patterns amongst adolescents in Australia

Labour force participation of adolescents in Australia is growing at an unprecedented rate. This increased participation is coupled with a growing realisation of the vulnerability of adolescents in the labour market in terms of occupational injury. Despite recent evidence that time of day may be an important determinant of adolescent injuries, the impact of non-standard and night work on adolescent injury rates has received scant attention to date. The current study addresses this shortcoming by examining injury patterns of 3201 working adolescents in Queensland. Results revealed that female adolescents are 2.5 times more likely to sustain an injury on day shift and 4.71 times more likely to sustain an injury on night shift than their adult counterparts when total work hours are taken into consideration. Similar results were found for male adolescents with an injury to work hours ratio of 2.19 on day shift and 3.05 on night shift. These findings point to the value of considering the temporal pattern of adolescent work in future research aimed at minimising injuries at work and improving the work experience of tomorrow's workforce.