Investigation of Centreline Segregation and Centreline Porosity in CC ‐ Slabs

Improvements of solidification processing in conventional or near net shape casting depend on sophisticated methods of macroscopic examination of central unsoundness and inhomogeneity of the cast material. As long as remarkable deviations referring to the quality features exist between the slab centre and the bulk material such methods should be looked after. Two semi‐macroscopic methods of segregation analysis are discussed in this paper: firstly, the potential of the emission spectral analysis combined with sectioning of a sample and secondly, the computer aided micro‐probe analysis. Both methods are restricted to small local areas. The proneness of elements and steel grades with respect to micro‐ and macrosegregation as well as distribution‐ and segregation coefficients are determined. The effect of a diffusion anneal on homogenisation of segregation is studied. Last not least the precipitation of sulphides, phosphides or carbonitrides is estimated. Ultrasonic detection of the central unsoundness of conventional slabs or thin slabs is a new approach towards a quantitative macroscopic examination. The sample size is 400 mm x 300 mm x 30 mm. To achieve a low ultrasonic noise using a 5 MHz‐transducer the samples are heat treated for grain refinement. “Dog bone porosity”, macroscopic topographical misfit between the upper and lower solidifying shells, periodical corner cracks or systematic transverse centre cracks are detected. Obviously, segregations do not reflect the ultrasonic beam. However, it can be presumed that an interrelation exists between porosity and segregation, although these might be locally apart from each other. This new ultrasonic test of the central unsoundness of continuous cast (c.c.) slabs gives valuable arguments to machine builders and maintenance people to decide on the right concepts for strand guidance, support rollers and secondary cooling. Soft reduction can be optimized and variations in casting speed counteracted by dynamic means. R&D service of this kind can help steelmakers when decisions are impending to build new casting machines or revamp old ones.     

Effect of sampling density on the measurement of stream condition indicators in two lowland Australian streams

There is widespread application of indicators to the assessment of environmental condition of streams. These indicators are intended for use by managers in making various comparative and absolute assessments and often have a role in resource allocation and performance assessment. Therefore, the problem of formally defining confidence in the results is important but difficult because the sampling strategies used are commonly based on a compromise between the requirements of statistical rigour and the pragmatic issues of access and resources. It is rare to see this compromise explicitly considered and consequently there is seldom quantification of the uncertainty that could affect the confidence a manager has in an indicator. In this paper, we present a method for quantitatively assessing the tradeoffs between sampling density and uncertainty in meeting various monitoring objectives. Assessments using judgement‐based representative reaches are shown to be unreliable; instead a sampling approach is recommended based on the random selection of measuring sites. A detailed dataset was collected along two streams in Victoria, Australia, and the effect of sampling density was assessed by subsampling from this dataset with precision related to the number of sites assessed per reach length and the intensity of the sampling at each site. The sampling scheme to achieve a given precision is shown to depend on the monitoring objective. In particular, three objectives were considered: (1) making a baseline assessment of current condition; (2) change detection; and (3) detection of a critical threshold in condition. Change detection is shown to be more demanding than assessing baseline condition with additional sampling effort required to achieve the same precision. Sampling to detect a critical threshold depends on nominating acceptable values of Type I and II error and the size of the effect to be detected. Copyright © 2006 John Wiley & Sons, Ltd.     

高分辨率合成孔径雷达图像舰船检测方法

寻找针对高分辨率SAR图像的舰船目标检测算法。利用KSW双阈值分割技术，其效果比传统检测方法好，有利于进一步的目标分类和识别。且必须根据SAR图像分辨率来选择舰船检测算法。     

信息化对制造业的影响综述

信息技术是一种新型技术。信息技术和制造业相互依存、相互促进是一种发展趋势。信息技术对制造业的影响主要有:信息化在制造业的产品开发设计技术方面带来的进步,这主要包括计算机辅助技术;信息化在制造业的生产管理技术方面带来的进步,主要包括企业制造资源计划系统及产品数据管理;信息化在制造业的制造方式上带来的进步,主要包括柔性制造、计算机集成制造等先进制造技术。     

Efficient similarity-based operations for data integration

Dealing with discrepancies in data is still a big challenge in data integration systems. The problem occurs both during eliminating duplicates from semantic overlapping sources as well as during combining complementary data from different sources. Though using SQL operations like grouping and join seems to be a viable way, they fail if the attribute values of the potential duplicates or related tuples are not equal but only similar by certain criteria. As a solution to this problem, we present in this paper similarity-based variants of grouping and join operators. The extended grouping operator produces groups of similar tuples, the extended join combines tuples satisfying a given similarity condition. We describe the semantics of this operator, discuss efficient implementations for the edit distance similarity and present evaluation results. Finally, we give examples of application from the context of a data reconciliation project for looted art.     

Cycle detection using a stack

We present an algorithm for detecting periodicity in sequences produced by repeated application of a given function. Our algorithm uses logarithmic memory with high probability, runs in linear time, and is guaranteed to stop within the second loop through the cycle. We also present a partitioning technique that offers a time/memory tradeoff. Our algorithm is especially well suited for sequences where the cycle length is typically small compared to the length of the acyclic prefix.     

Pedestrian detection and tracking at crossroads

This paper presents a system that can perform pedestrian detection and tracking using vision-based techniques. A very important issue in the field of intelligent transportation system is to prevent pedestrians from being hit by vehicles. Recently, a great number of vision-based techniques have been proposed for this purpose. In this paper, we propose a vision-based method, which combines the use of a pedestrian model as well as the walking rhythm of pedestrians to detect and track walking pedestrians. Through integrating some spatial and temporal information grabbed by a vision system, we are able to develop a reliable system that can be used to prevent traffic accidents happened at crossroads. In addition, the proposed system can deal with the occlusion problem. Experimental results obtained by executing some real world cases have demonstrated that the proposed system is indeed superb.     

An immune oriented multi-agent system for biological image processing

In this article, we present a parallel image processing system based on the concept of reactive agents. Our system lies in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features. We also present a method of segmentation using a multi-agent system, and two biological applications made with oRis. The stopping of this multi-agent system is implemented through a technique issued from immunology: the apoptosis.     

Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.     

Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.