Geometric shape errors in forging: developing a metric and an inverse model

The complexity of the forging process ensures that there is inherent variability in the geometric shape of a forged part. While knowledge of shape error, comparing the desired versus the measured shape, is significant in measuring part quality the question of more interest is what can this error suggest about the forging process set-up? The first contribution of this paper is to develop a shape error metric which identifies geometric shape differences that occur from a desired forged part. This metric is based on the point distribution deformable model developed in pattern recognition research. The second contribution of this paper is to propose an inverse model that identifies changes in process set-up parameter values by analysing the proposed shape error metric. The metric and inverse models are developed using two sets of simulated hot-forged parts created using two different die pairs (simple and 'M'-shaped die pairs). A neural network is used to classify the shape data into three arbitrarily chosen levels for each parameter and it is accurate to at least 77 per cent in the worst case for the simple die pair data and has an average accuracy of approximately 80 per cent when classifying the more complex 'M'-shaped die pair data.

Priority frequency maptable for query optimization using materialized views

In this paper query optimization using materialized views has been analyzed and a comprehensive and efficient technique has been proposed to create Map-table. Materialized views can provide massive improvements in query processing time, especially for aggregation queries over large tables. To realize this potential, a number of existing techniques have been considered regarding the problem of maintaining materialized views as well as optimal searching time and memory overhead. Keeping this in mind, an optimal algorithm has been proposed in this paper for query optimization. It has been demonstrated that the proposed algorithm reduces the searching time substantially and reducing the memory size as well.

Integrating errors into developmental assessment: 'time' for ages 8-13

It is widely agreed that measurement is of paramount importance to students’ overall development in mathematics. This paper describes a developmental ‘map’ of students’ understanding and skills in measurement, focussed on the topic of Time, that integrates correct and incorrect student ideas. The map is based on a Rasch analysis of data from a large-scale UK national survey for standardising assessment for children from 5 to 14 years of age. It is demonstrated how a partial credit strategy enables a developmental map to be constructed to show students’ strengths and weaknesses in a meaningful and useful summative and formative manner. This map provides evidence, of both a summative and a formative nature, which may enable teachers to craft appropriate and successful learning experiences for children.

The ethics and practical importance of defining, distinguishing and disclosing nursing errors : a discussion paper

Nurses globally are required and expected to report nursing errors. As is clearly demonstrated in the international literature, fulfilling this requirement is not, however, without risks. In this discussion paper, the notion of ‘nursing error’, the practical and moral importance of defining, distinguishing and disclosing nursing errors and how a distinct definition of ‘nursing error’ fits with the new ‘system approach’ to human-error management in health care are critiqued. Drawing on international literature and two key case exemplars from the USA and Australia, arguments are advanced to support the view that although it is ‘right’ for nurses to report nursing errors, it will be very difficult for them to do so unless a non-punitive approach to nursing-error management is adopted.

Analysis of children's merging errors

Array

Medical image retrieval with query-dependent feature fusion based on one-class SVM

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

Image retrieval using noisy query

In conventional content based image retrieval (CBIR) employing relevance feedback, one implicit assumption is that both pure positive and negative examples are available. However it is not always true in the practical applications of CBIR. In this paper, we address a new problem of image retrieval using several unclean positive examples, named noisy query, in which some mislabeled images or weak relevant images present. The proposed image retrieval scheme measures the image similarity by combining multiple feature distances. Incorporating data cleaning and noise tolerant classifier, a twostep strategy is proposed to handle noisy positive examples. Experiments carried out on a subset of Corel image collection show that the proposed scheme outperforms the competing image retrieval schemes.

QDFA : query-dependent feature aggregation for medical image retrieval

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

A new query dependent feature fusion approach for medical image retrieval based on one-class SVM

With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.